rolling forcast

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销售定义– Forecast 状态

销售定义–  Forecast 状态

•Account Plan reviewed and posted •Partner info entered to acct •Cust Network Topology posted •Updated oppty with estimated tech, $, timeline
立 项 • Cust executive
谈 判 邀 标
•Customer Agreement to move forward •Verbal agreement that Bangcle solution is accepted •Customer confirmation of steps required to close •Finalized config & quote
Objectives
发 现
Verifiable Outcomes
•Known customer issues – compelling enough to drive action •Customer committed to investigating the fit of a Bangcle solution
Judgment / Adjustment
“Commit”
Commit Opptiesties
“None”
All Other Oppties
Committed Forecast Best Case Pipeline Funnel
Forecast Status: • Commit: Will book to Bangcle within forecast period • Upside : May potential book to Bangcle within forecast period • None: Not expected to book to Bangcle within forecast period • Funnel : All potential opportunity does not including judgment / adjustment • Judgment / Adjustment: Adjustment towards forecast $ Opportunity Status: • Active: Work in progress, not closed • Booked: Bangcle booked • Cancelled: Lost Funding; Oppty no longer exist; Out of business; Merged with other company • Lost: Lost to competitor • Error: Entered in error

CUACE模式在兰州城市空气质量预报中的检验订正

CUACE模式在兰州城市空气质量预报中的检验订正

CUACE模式在兰州城市空气质量预报中的检验订正何金梅;刘抗;王玉红;张培燕【摘要】利用2014年9月-2015年8月环境保护部对外发布的兰州市6种污染物实况监测数据,对同时期CUACE模式的24 h预报结果进行误差分析,并通过误差滚动线性回归订正方法进行检验订正.结果表明:(1)兰州市的首要污染物以PM10为主,其次是PM2.5;(2) CUACE模式对SO2的预报及对O3、NO2、PM2.5和PM10预报为2级时,等级预报准确率较高,预报结果可直接使用;(3)模式对O3和CO预报1级时,采用10d误差滚动订正后等级预报准确率可提高1.1%~5.5%;(4)模式预报其它要素的其它级别时采用5d或10 d误差滚动订正后再加上或减去一定值后,等级预报准确率可提高8.7%~75%.%Based on the observation data of six kinds of pollutants issued by the Ministry of Environmental Protection from September 2014 to August 2015 in Lanzhou of Gansu Province,the deviation of 24 h forecast results of the CUACE model was analyzed firstly,then the test and correction were carried out by using the error rolling linear regression correction method.The results are as follows:(1) The primary pollutant in Lanzhou was PM10 and followed by PM2.5.(2) For SO2,the forecast results of the CUACE model could be used directly.For 03 、NO2 、PM2.5 and PM10,when the forecast values were level 2,the accuracy of forecast results was higher,which could be used directly also.(3) When the forecast values of the CUACE model were level 1 for O3 and CO,the accuracy of level forecast could be increased by 1.1%-5.5 % after the deviation correction based on ten-day error rolling linear regression correction method.(4) For other elements,when the forecastvalues of the CUACE model were other levels,the five-day or ten-day error rolling linear regression correction method was adopted to correct the deviation,then add or subtract a certain value,the accuracy of level forecast would be increased by 8.7%-75%.【期刊名称】《干旱气象》【年(卷),期】2017(035)003【总页数】7页(P495-501)【关键词】CUACE模式;误差分析;检验订正;兰州市【作者】何金梅;刘抗;王玉红;张培燕【作者单位】甘肃省气象服务中心,甘肃兰州730020;甘肃省气象服务中心,甘肃兰州730020;甘肃省酒泉市气象局,甘肃酒泉735000;甘肃省气象服务中心,甘肃兰州730020【正文语种】中文【中图分类】P457随着中国城市化进程的加快,区域灰霾污染现象频繁发生,特别是2013年1月我国中东部和西南部接连发生罕见的大范围、长时间雾霾天气过程,给人们的健康和生活带来诸多不便,引起政府和公众的广泛关注,使空气质量预报研究再次成为人们研究的热点之一[1]。

统计预测与决策教案

统计预测与决策教案

统计预测与决策教案时间:2005年9月管理预测与决策方法授课计划•定性预测方法•定量预测方法◆确定性方法回归分析预测方法时间序列平滑预测方法趋势外推预测方法马尔可夫预测与决策法◆不确定性方法灰色系统预测随机性决策分析模糊决策粗糙集理论第一章预测概述1.1 引言1. 预测的兴起预测于20世纪60-70年代在美国逐步兴起的预测:预测是指对事物的演化预先做出的科学推测。

广义的预测,既包括在同一时期根据已知事物推测未知事物的静态预测,也包括根据某一事物的历史和现状推测其未来的动态预测。

狭义的预测,仅指动态预测,也就是指对事物的未来演化预先做出的科学推测。

预测理论作为通用的方法论,既可以应用于研究自然现象,又可以应用于研究社会现象,如社会预测、人口预测、经济预测、政治预测、科技预测、军事预测、气象预测等。

2. 预测的作用正确的预测是进行科学决策的依据。

政府部门或企事业单位制定发展战略、编制计划以及日常管理决策,都需要以科学的预测工作为基础。

如“诸葛亮借东风、空城计”、以美国为首的多国部队实施的“沙漠风暴”,研究人员建立了热能转换模型,进行了一系列模拟计算。

因此,人们说第一次世界大战是化学战(火药),第二次世界大战是物理战(原子武器),而海湾战争是数学战,指的是这场战争在战前就已对战争的进程以及战争所涉及和影响的方方面面做出了科学预测。

制订经济计划的依据之一提高经济效益的手段之一提高管理水平的途径之一1.2 预测的基本原则1. 坚持正确的指导思想2. 坚持系统性原则预测者所研究的事物和自然界的其他事物一样,都有自己的过去、现在和将来,就是存在着一种纵的发展关系,因果关系,而这种因果关系要受某种规律的支配。

将事物作为一个互相作用和反作用的动态整体来研究,而且要将事物本身与周围的环境组合成一个系统综合体来研究。

例如:1943年全世界估计有三亿疟疾病患者,每年有300万人死亡,4500万人死于瘟疫,1945年后使用了DDT,十年内疟疾病的死亡率降低了二分之一,瘟疫病患者每年仅死亡几千人。

滚动计划法的实施步骤

滚动计划法的实施步骤

滚动计划法的实施步骤引言滚动计划法(Rolling Forecast)是一种灵活的预测和规划方法,它能够适应市场的变化和组织的需求。

本文将介绍滚动计划法的实施步骤,以帮助组织更好地进行预测和规划。

步骤一:制定目标首先,组织需要明确滚动计划法的目标。

目标可以是提高预测准确性、降低库存成本、提升客户满意度等。

制定明确的目标能够帮助组织在实施滚动计划法时有所侧重,并更好地评估实施效果。

步骤二:确定关键指标在实施滚动计划法时,组织需要确定关键指标来衡量预测和规划的准确性和有效性。

常见的关键指标包括预测偏差、库存周转率、订单交付率等。

通过监控关键指标的变化,组织可以及时发现问题并采取相应措施。

步骤三:收集历史数据为了进行有效的预测和规划,组织需要收集历史数据。

历史数据可以包括市场需求、销售数据、供应链信息等。

收集历史数据的方式可以通过内部系统、市场调研、客户反馈等途径。

收集到的历史数据将为后续的预测和规划提供基础。

步骤四:建立预测模型在收集到历史数据后,组织需要建立合适的预测模型。

预测模型可以基于统计学方法、机器学习算法等。

选择合适的预测模型需要根据组织的需求和数据特征来进行。

建立好的预测模型将为滚动计划提供可靠的预测结果。

步骤五:制定滚动计划周期滚动计划法的核心是根据市场需求的变化和组织的调整能力,制定滚动计划周期。

滚动计划周期可以根据产品生命周期、市场变化速度等因素来确定。

通常,滚动计划周期会比传统的固定计划周期要短,以更好地适应市场的变化。

步骤六:进行滚动预测和规划按照制定的滚动计划周期,组织开始进行滚动预测和规划。

首先,利用建立好的预测模型对未来一段时间的市场需求进行预测。

然后,根据预测结果对供应链进行规划,包括原材料采购、生产安排、配送计划等。

步骤七:实施和监控在滚动计划的实施过程中,组织需要及时调整和监控预测和规划的执行情况。

通过实施和监控,组织可以发现和解决潜在问题,保证预测和规划的准确性和有效性。

Planning-MTS

Planning-MTS

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3/3
4. 5.
Solution design -procedures Solution design- it tools
Edward,gao tw, yangjian, gaolin Yangjian, gao lin
Asm/rsms, leo Xia qishu
Process charts Excel/acce ss, programs Kpi, org chart Feedback reports
complexity and emergent demand. – Span of control:enlarged span of control along the whole supply chain
3
Problems In Sales Forecasting- a dead lock
No complete information reference for forecast •Detail historical sales record •Demand situation •Competition status •Sell in and sell out Poor forecasting skills, low commitment and seriousness 1. Rough idea about historical sales and market growth as the basis of forecasting . • Lack of market research tools and scientific study of the competition, customers buying behavior etc. • Lack of tools to consolidate and analyze the historical sales record by different dimensions. Forecast is more as “top down” instead of “bottom up and then top down approach • Top down in most cases by budget and growth plan, weak in collecting, storing, consolidating and analyzing the requirements from the real market and channels. • Forecast based on feeling, no accurate information on the inventory of the channels. Lack of systematic analysis, control, testing and forecast of new product launching. Weak in analyzing the product life cycle, target customer, sales requirements Lack of cohesive collaboration between sales, marketing, 4 depts production and other

滚动计划准确率考核

滚动计划准确率考核

滚动计划准确率考核英文回答:Rolling forecast accuracy assessment is an important aspect of evaluating the effectiveness of a forecasting plan. It involves measuring the accuracy of forecasts made at different points in time and comparing them to the actual outcomes. This assessment helps in identifying any deviations between the forecasted values and the actual values, allowing for adjustments to be made to improve future forecasts.There are several key steps involved in conducting a rolling forecast accuracy assessment. Firstly, historical data is collected for the period under consideration. This data includes both the forecasted values and the corresponding actual outcomes. The forecasted values can be obtained from previous forecasting models or from expert opinions.Next, the accuracy of the forecasts is measured using appropriate statistical metrics. Commonly used metrics include mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). These metrics provide a quantitative measure of the forecast error, allowing for easy comparison across different time periods.After calculating the accuracy metrics, a comparison is made between the forecasted values and the actual outcomes. This helps in identifying any patterns or trends in the forecast errors. For example, if the forecasts consistently overestimate or underestimate the actual values,adjustments can be made to the forecasting model or process.Furthermore, it is important to analyze the reasons behind any forecast errors. This analysis can help in understanding the factors that contribute to inaccuracies and guide improvements in the forecasting process. For example, if certain variables are consistently overlookedor if there are external factors that impact the accuracyof the forecasts, these can be identified and addressed.Based on the findings of the rolling forecast accuracy assessment, adjustments can be made to the forecasting plan. This may involve refining the forecasting models, updating the data sources, or incorporating additional variablesinto the models. The aim is to continuously improve the accuracy of the forecasts and ensure that they align with the actual outcomes.In conclusion, a rolling forecast accuracy assessmentis a valuable tool for evaluating the effectiveness of a forecasting plan. By measuring the accuracy of forecasts at different points in time and comparing them to the actual outcomes, adjustments can be made to improve future forecasts. This assessment helps in identifying any deviations between the forecasted values and the actual values, allowing for continuous improvement in the forecasting process.中文回答:滚动计划准确率考核是评估预测计划有效性的重要方面。

9.客户拜访纪要030722.doc

9.客户拜访纪要030722.doc

TO: 叶明、林洪藩、王德才、黄佩群Ye Ming, Lin Hong Fan, Wang De Chai, Huang YanFROM: 陈宝明Paul RosenblumD ATE: 24 July 2003SUBJECT主题: 客户拜访—国美电器Customer Visit – GOME Appliance Store在康佳沈阳分公司业务人员何晓东陪同下,我与钟国卫拜访了沈阳国美电器公司的市场部主管段岩。

从以下对话中可以了解到我们此行的目的:Accompanied by account manager Miss He, Weiky Zhong and I visited Mr Tuan Yuan, Marketing Supervisor of the newest GOME store opened in Shenyang. Our purpose will be evident from the following questions asked:陈宝明/钟国卫:康佳非常看重与国美现有的业务关系,并渴望能进一步提高双方的信息交流与共享,以使康佳更加高效地满足国美的需求,促进双方共同利益。

康佳认同现有一位业务员专职跟踪国美日库存量与补货要求的工作方式,但作为双方共同提高库存计划与货源供应的第一步,康佳希望能更早地了解国美的库存需求。

作为开始,我们建议开始实行一个每周更新的四周需求预测,您同意这种做法将对双方都有利吗?Paul/Weiky: Konka values its existing business relationship with GOMe and desires to improve information exchange i n order to satisfy GOME’s requirements more effectively for mutual benefit. Konka appreciates the current practice whereby a Konka employee monitors GOME’s daily inventory levels and solicits a replenishment order as necessary. However, as a first step towards mutuallyimproving inventory planning and product availabilty, Konka would like to know GOME’s inventory requirements further in advance. To start we suggest a rolling 4 week forecast. Do you agree that this would be beneficial ?段岩:我同意,也愿意提供每周更新的四周预测。

工厂常见英语术语及略缩语

工厂常见英语术语及略缩语

1. Sales Department:Assembly line 组装线Layout 布置图- Sales order 销售定单Conveyer 流水线物料板- Sales order release form 生产通知单Rivet table 拉钉机- sales rolling forecast (滚动式)销售预测Rivet gun 拉钉枪- bit tender 投标Screw driver 起子- contract terms 合同条款Pneumatic screw driver 气动起子- payment terms 付款方式worktable 工作桌- product specification 产品规格OOBA 开箱检查- delivery schedule 交期fit together 组装在一起- on time delivery 准时交货fasten 锁紧(螺丝)- short order 缺货fixture 夹具(治具)- build to stock 按库存排生产pallet 栈板- build to order 以销定产barcode 条码- contract amendment 合同修订barcode scanner 条码扫描器- order confirmation 合同确认fuse together 熔合- sales officer 销售员fuse machine热熔机repair修理2. PMC (Purchasing & Material Control)operator作业员QC品管- Purchase requisition 采购申请supervisor 课长- Purchasing order 采购定单ME 制造工程师- Supplier 供应商MT 制造生技- Vendor/buyer 卖主cosmetic inspect 外观检查- Subcontractor 分承包商inner parts inspect 内部检查- Supplier evaluation 供应商评审thumb screw 大头螺丝- Buyer 买家lbs. inch 镑、英寸- Stock level 库存量EMI gasket 导电条- Stock register 库存记录front plate 前板- Material released request 物料发货单rear plate 后板- Product released request 产品发货单chassis 基座- Lead time (for purchase order) 交期bezel panel 面板#NAME?power button 电源按键- (products) over due of the shelf life 过期reset button 重置键- Re-order point 再采购期Hi-pot test of SPS 高源高压测试- Safety stock/Safety stock level 安全库存/安全库存量Voltage switch of SPS 电源电压接拉键- Cycle time 库存周转期sheet metal parts 冲件- Storage condition plastic parts 塑胶件- Store Reeper 仓管员SOP 制造作业程序material check list 物料检查表3. Production work cell 工作间trolley 台车- production planning 采购计划carton 纸箱- product specification 产品规格/标准sub-line 支线- processing specification 过程控制程序left fork 叉车- product cycle time 生产周期personnel resource department 人力资源部- standard time 工时定额production department生产部门- production capacity 产能planning department企划部- production volume 产量QC Section品管科- machine utilization 机器正常运转率stamping factory冲压厂- machine down time 停机率painting factory烤漆厂- operating condition 生产操作molding factory成型厂- Humidity 湿度common equipment常用设备- Moisture level 湿度率uncoiler and straightener整平机- Dust level 尘埃水平punching machine 冲床- Dust count 尘量robot机械手- Formula (物料)配方hydraulic machine油压机- proportion of the ingredients 配料比例lathe车床- bill of materials 物料清单planer |plein|刨床- control standard 控制指标miller铣床- control parameter 控制参数grinder磨床- upper control limit 上限linear cutting线切割- lower control limit 下限electrical sparkle电火花- tolerance 误差值welder电焊机- variation 被动值staker=reviting machine铆合机- process capability and stability 过程能力/稳定性position职务- first article or last article inspection 首件检测president董事长- confirmation of process 工序过程确认general manager总经理- process flow 流程special assistant manager特助- process flow chart 流程图factory director厂长- traveller card 随机卡department director部长- tag 标牌deputy manager | =vice manager副理- transit store 周转仓section supervisor课长- product & process identification 产品/过程标识deputy section supervisor =vice section superisor副课长- traceability 追溯性group leader/supervisor组长- Production line 生产线line supervisor线长- operator/supervisor 操作工/领班assistant manager助理- normal operating condition 正常操作条件to move, to carry, to handle搬运- shift 班次be put in storage入库- passed for inspection 通过检验pack packing包装- acceptance /rejection rate 合格/不合格率to apply oil擦油- rejected and accepted products 不合格/合格产品to file burr 锉毛刺- failure to meet the requirement 未达到要求final inspection终检- the control standard should be…. but was found to be….控制指标应为…实际为…to connect material接料- production yield 生产完工率to reverse material 翻料- temperature profile 湿度曲线wet station沾湿台- under control condition 受控条件下Tiana天那水- out of control 超标cleaning cloth抹布- regular processing monitoring 定期工序监控to load material上料to unload material卸料4. Quality control to return material/stock to退料scraped |'skr?pid|报废- IQC scrape ..v.刮;削- incoming inspection 进料检验deficient purchase来料不良- sampling plan 抽样计划/抽样方案manufacture procedure制程- sample size 抽样量deficient manufacturing procedure制程不良- sampling method 抽样方法oxidation |' ksi'dei?n|氧化- acceptable quality level 允收标准(仅针对抽样检验)scratch刮伤- inspection instruction 检验指导书dents压痕- visual inspection 外观检验defective upsiding down抽芽不良- defect 缺陷defective to staking铆合不良- scratch 划痕/划伤embedded lump镶块- burs 毛刺/毛边feeding is not in place送料不到位- product sample 产品样stamping-missing漏冲- crack 裂缝production capacity生产力- void 空缺处education and training教育与训练- dimensional measurement 尺寸测量/尺寸检查proposal improvement提案改善- testing 测试spare parts=buffer备件- testing parameter 测试参数forklift叉车- drop test 跌落试验trailer=long vehicle拖板车- tensile strength/tensile strength 张力强度/拉力强度compound die合模- elongation test/elongation rate 拉伸测试/拉伸率die locker锁模器- high pot test 高压(电压)测试pressure plate=plate pinch压板- burning test 烧机bolt螺栓- reliability test 可靠性试验administration/general affairs dept总务部- test rejection 拒收automatic screwdriver电动启子- pending for disposition 等待处理thickness gauge厚薄规- waiting for inspection 等待检验gauge(or jig)治具- disposition of rejected products 拒收产品的处理power wire电源线- out of specification 超标buzzle蜂鸣器- rework 返工defective product label不良标签- waive 放弃/报废identifying sheet list标示单- waive inspection 免检location地点- compulsory test items 强制检验/测试项目present members出席人员subject主题indication 缺陷conclusion结论service 保养,维护,维修decision items决议事项field 现场 in the field responsible department负责单位rounded indication 点状缺陷pre-fixed finishing date预定完成日solvent 溶剂approved by / checked by / prepared by核准/审核/承办layout 划线PCE assembly production schedule sheet PCE组装厂生产排配表finish 粗糙度model机锺flatness 不平度work order工令bearing housing 轴承箱revision版次dial indicator 千分表remark备注laser topography 激光测量production control confirmation生产确认environmental regulation 环境法checked by初审burr 毛刺approved by核准localized pitting 部分点蚀department部门etch 腐蚀stock age analysis sheet 库存货龄分析表crack 裂纹on-hand inventory现有库存shrinkage 缩孔available material良品可使用peening 锤击obsolete material良品已呆滞Engineering’s 技术部门to be inspected or reworked 待验或重工shielded carbon arc welding 碳弧保护焊, 气体保护碳极电弧焊total合计emissivity paint 辐射涂料cause description原因说明rest 支架part number/ P/N 料号thermocouple 热电偶type形态truth spot (工件上的)检验点item/group/class类别thermal treatment 热处理quality品质stress relief 应力释放prepared by制表 notes说明mechanical testing 机械性能试验year-end physical inventory difference analysis sheet 年终盘点差异分析表index 分度physical inventory盘点数量test specimen 试样physical count quantity帐面数量thermal stability test 热稳定试验difference quantity差异量reading 读数cause analysis原因分析forging 锻件raw materials原料casting 铸件materials物料deflection 挠度finished product成品plating 电镀semi-finished product半成品chromium plating 镀铬packing materials包材wear 磨损good product/accepted goods/ accepted parts/good parts良品luster 光泽度defective product/non-good parts不良品magnetic particle examination 磁粉探伤disposed goods处理品dry visible magnetic particle examination 干法非荧光磁粉探伤warehouse/hub仓库wet fluorescent magnetic particle examination 湿法荧光探伤on way location在途仓blazing 钎焊oversea location海外仓induction blazing 感应钎焊spare parts physical inventory list备品盘点清单Stellite 硬质合金spare molds location模具备品仓nitric acid 销酸skid/pallet栈板radiographic examination 射线探伤tox machine自铆机liquid penetrant examination 液体渗透探伤wire EDM线割ultrasonic examination 超声波探伤EDM放电机shot peening 喷丸coil stock卷料packing piece, packing section, caulking piece,caulking section填隙条sheet stock片料clearance fit 间隙配合tolerance工差interference fit 过盈配合score=groove压线descaling 除锈cam block滑块coating 涂层,覆层,镀层pilot导正筒cold drawing 冷拉(件)trim剪外边shot blasting 喷丸(处理)pierce剪内边grit blasting 喷钢砂(处理)drag form压锻差sand blasting 喷砂(处理)pocket for the punch head挂钩槽bar 棒材slug hole废料孔stock 原料feature die公母模lubricant 润滑剂expansion dwg展开图rolling mill,roll machine 轧钢厂,轧钢机,滚轧机radius半径draw benche 拉床shim(wedge)楔子gas fired furnace 煤气加热炉torch-flame cut火焰切割shear machine 剪切机set screw止付螺丝straightening roll machine 辊式矫直机form block折刀decarburization 脱碳stop pin定位销Charpy impact text 夏比冲击试验round pierce punch=die button圆冲子fatigue 疲劳shape punch=die insert异形子die 模具stock locater block定位块tensile testing 拉伸试验under cut=scrap chopper清角solution 固溶处理active plate活动板aging 时效处理baffle plate挡块cold header冷墩机cover plate盖板grub screw 平头螺钉]male die公模elongation 延伸率female die母模rivet 铆钉groove punch压线冲子groove 槽air-cushion eject-rod气垫顶杆Rockwell hardness 洛低硬度spring-box eject-plate弹簧箱顶板Brinell hardness 布氏硬度bushing block衬套ferrite 铁素体insert 入块stand 架子club car高尔夫球车sheeter 轧板机capability能力roll 辊子parameter参数convex 凸状,凸面factor系数concave 凹, 凹面,凹板phosphate皮膜化成guide board 导板viscosity涂料粘度pitch gauge 螺距规alkalidipping脱脂guillotine shear 剪板机main manifold主集流脉descale 除污,除氧化皮等。

What is Rolling Forecast

What is Rolling Forecast

What is Rolling ForecastBudgeting and Forecasting with a rolling forecastForecasting like budgeting involves future projections. Traditionally businesses look at astatic period for example 1 year ahead. If the financial year is July to June then budgetsand forecasts are prepared well in advance to cover that period. By October, 3 monthsof trading has already taken place leaving only 9 months remaining of the forecast. Withstatic forecasts you run the periods down to zero and then start again.With a rolling forecast the number of periods in the forecast remain constant so that if for example the periods of your forecast are monthly for 12 months then as each month istraded it drops out of the forecast and another month is added onto the end of theforecast so you are always forecasting 12 monthly periods out into the future.The number of periods in a rolling forecast remainsconstant e.g. the original forecast if for 12 periodsand the period length is monthly from July to June.As actual trading amounts become available for aperiod it moves from being a future prediction to acurrent reality i.e. it is no longer part of the forecastso it drops out of the forecast and another month isadded onto the end of the forecast (you roll theforecast forward one month) so you are alwaysforecasting 12 months out into the future.Benefits of rolling forecast.In the modern world business conditions are volatile and change rapidly. There is littlefuture for a business not in a position to respond quickly to external market changes soflexible planning processes are vital to rise to the challenge. This is where theeffectiveness of rolling forecasts in the planning process are most keenly felt. With rolling forecasts you keep a finger on the pulse of changing conditions and can quickly refocusthe business accordingly e.g. decisions on which projects to scrap and which to invest in can be made timeously.Obviously the speed and accuracy of your financial forecasting will have a bearing on the profit of the organization. These factors can be ehanced by using the correct tool for the job. Like budgeting there are basically two approaches for rolling forecasts. You can either use a spreadsheet or some spreadsheet based solution with all its inherent risks and defects, or you can use dedicated solution like Visual Cash Focus budgeting and forecasting software. The huge advantage of a dedicated solution lies in the fact that it is not human resource reliant. Anyone who has been left to pick up the pieces of a spreadsheet started by someone else will be all to familiar with the perils inherent in trying to unravel the "masterpiece" of a colleague no longer available for consultation. Also financial specialists generally have very different skill sets from those necessary for complex spreadsheet programming and its not the best use of their skills to waste time doing something outside their specialized skill set.Annual budget rounds are resource intensive in time and money, need to be prepared well in advance of the planning periods under review and are often outdated from changing market conditions pretty early on in the time frame. With Visual Cash Focus the last period of the forecast is rolled forward as the basis for new period so the managers are always reviewing the most up to date predictions for the rolling forecasts. If your forecast periods are monthly and you roll the forecast 12 times then the following years forecast is complete without the need for another budget round so a rolling forecast can eliminate the annual budget process.Probably the most important beneficiary of a good rolling forecast is having an accurate handle on likely future cash flows in the business. No longer is the income statement a sufficient measure of viability or being able to turn to banks for funding cash shortfalls a certainty. Since the global financial crisis bank credit is in very short supply and unless the business has its own cash reserves the road ahead is far from secure.。

Rolling budget and forecast1

Rolling budget and forecast1

$225,000 $218,000 ($7,000) $185,000 $33,000 $170,000 $165,500 $4,500 $147,000 ($18,500) $15,000 $17,000 ($2,000) $18,000 $1,000 $115,000 $123,000 ($8,000) $75,000 ($48,000) $31,000 $31,000 $0 $18,000 ($13,000) $15,000 ($6,000) ($21,000) ($57,000) $51,000
$120,000 $120,000 $0 $110,000 $10,000 $90,000 $90,000 $0 $95,000 $5,000 $8,000 $8,000 $0 $8,000 $0 $50,000 $50,000 $0 $37,000 ($13,000) $15,000 $15,000 $0 $13,000 ($2,000) $15,000 $15,000 $0 ($30,000) $45,000
$85,000 $88,000 $3,000 $70,000 $18,000 $65,000 $63,000 $2,000 $54,000 ($9,000) $4,000 $3,000 $1,000 $5,000 $2,000 $40,000 $42,000 ($2,000) $30,000 ($12,000) $11,000 $12,000 ($1,000) $7,000 ($5,000) $5,000 $2,000 ($3,000) ($27,000) $29,000
$100,000 $100,000 $0 $95,000 $5,000 $75,000 $75,000 $0 $80,000 $5,000 $8,500 $8,500 $0 $8,000 ($500) $45,000 $45,000 $0 $35,000 ($10,000) $15,000 $15,000 $0 $12,000 ($3,000) $12,500 $12,500 $0 ($25,000) $37,500

DS-DB数字系列分布式板及音频控制系统用户手册说明书

DS-DB数字系列分布式板及音频控制系统用户手册说明书

DS-DBDigital Series Distribution BoardDN-60565:BGeneralThe DS-DB Digital Series Distribution Board and its associ-ated amplifiers provide bulk amplification capability to theDigital Voice Command (DVC) system while retaining digitalaudio distribution capabilities. Up to four DS-AMP/E amplifi-ers can supply high-level risers spread throughout an instal-lation.The DS-DB converts digital audio to analog and routes it toaudio amplifiers and optional backups. The amplifiers sendback high-level audio, which the DS-DB routes to its risers.Control and status information passes between the DS-DBand its components via DS-BUS. The DS-DB communicateswith the rest of the digital audio system through the DAL (dig-ital audio loop), and takes up two of the 32 DAL addresses. Itmay be mixed with other devices on the same DAL, such asthe DAA2 and DAX series amplifiers.An optional Firefighters’ telephone riser on the DS-DB sup-ports local and network FFT communications.Features•Input capacity of four digital audio channels.•Four low-level audio outputs for connection to amplifiers in the same cabinet.•Eight high-level audio inputs (four primary, four backup), each input capable of handling 125W of audio at 25V RMS or 100W at 70.7V RMS when used with DS-AMP.•Four Class A/eight Class B high-level 125W audio outputs, each of which can output all 125 watts from any one of the four high level primary inputs or four high-level backup inputs when used with DS-AMP.•T wo digital audio loop wire ports, which may be modified to single- or multi-mode fiber ports with fiber option mod-ules.•Local FFT riser, capable of acting as a connection on the digital FFT riser.•DS-BUS interface to communicate with local bulk amplifi-ers and power supplies.•Up to 106 seconds of standard quality backup digital mes-sage storage (from VeriFire T ools® message library, or created by the installer) for use in the event of communica-tion loss with the DVC.•Isolated alarm bus input, to be used for backup activation of alarm messages when normal communication with the DVC is lost.•Audio output activation via network control-by-event equa-tions resident within the DVC.•USB port for VeriFire Tools® communication.•Uploads and downloads via the DVC.•24 VDC input for local power.•Works with AMPS-24 power supply and battery charger.InstallationThe DS-DB arrives from the factory already installed on its chassis. The DS-DB mounts in a CAB-4 Series cabinet, as well as in an EQCAB Series backbox.One or two fiber option modules will plug directly onto a DS-DB for simple installation. A DS-BDA backup amplifier mounts directly onto a DS-DB.Specifications24 VDC Input (TB24): 0.6A alarm or standby, non-resettable. Power-limited by source, supervised. Any device connected to TB24 must be installed in the same enclosure, or within the same room in conduit.Digital Audio Ports, wire media, A and B: EIA-485 proto-col, power-limited. Maximum distance per segment is 1900 feet (579.12m) on Belden 5320UJ (18AWG, TP) FPL cable: 18AWG (0.821 mm2) twisted-pair, unshielded, power-limited. For approved cable types, see wiring documentation, PN 52916ADD, Approved Wire Cables for Digital Audio Loops. Digital Audio Ports, fib er media, fib er option modules: Digital audio loop connectors support single- and multi-mode fiber with the use of fiber option modules. Refer to the fiber option datasheet for fiber specifications.Alarm Bus: Power-limited, supervised by source. Recom-mended wiring: 14-18 AWG twisted-pair. Requires 16VDC 24VDC.FFT Riser: Power-limited output, supervised. Class A or Class B operation. Class B 2-wire connections require a 3/9k ohm 1/2 watt resistor (PN R-3.9K). Max. wiring resistance (including individual telephone zone to last handset) permit-ted is 50 ohms, 10,000 ft (3048 m) max. wiring distance at 14 AWG to last handset.DS-BUS: EI A-485 protocol, power-limited. DS-BUS points must be installed in the same enclosure or within the same room in conduit. End points require end-of-line resistors.•DS-DB endpoint: set termination switch (SW8) to ON.•DS-AMP endpoint: add 120 ohm resistor on empty TB1terminals.DN-60565:B • 11/17/2014 — Page 1 of 2Page 2 of 2 — DN-60565:B • 11/17/2014NOTIFIER® and VeriFire® Tools are registered trademarks of Honeywell International Inc.©2014 by Honeywell International Inc. All rights reserved. Unauthorized use of this document is strictly prohibited.This document is not intended to be used for installation purposes. We try to keep our product information up-to-date and accurate. We cannot cover all specific applications or anticipate all requirements.All specifications are subject to change without notice.For more information, contact Notifier. Phone: (203) 484-7161, FAX: (203) 484-7118.•AMPS-24 endpoint: resistor is present, and power supply must be an endpoint.Use 14-18 AWG, twisted unshielded wire.Audio Out: Power-limited outputs (exception: an output pro-grammed for “Riser Mode to Control Modules”, “Riser Mode to RSM-AI M Series Modules”, or “Riser Mode to CI M/CSM Series Modules” is non-power-limited.)Up to 125 Watts out-put. Supervision determined by programming. 25V RMS or 70V RMS , depending on amplifier setting. Class A or Class B operation. Class B requires 20k end-of-line resistors (included, PN ELR-20K). Class A required 10k end-of-line resistors (included, PN R-10K) on the return. 12-18 AWG twisted-pair (shielded recommended).Primary and Backup 1 through 4: Four low-level audio out-puts for connection to amplifiers. Non-power-limited inputs.Supervision programmable. Amplifiers must be installed in the same enclosure or within the same room in conduit. Rec-ommended wiring: 14-18 AWG, twisted-pair, unshielded.OUT: Four DVC-AO-level audio outputs for connection to amplifiers. Power-limited. Supervision programmable. Ampli-fiers must be installed in the same enclosure or within the same room in conduit. 14-18 AWG, twisted-pair, unshielded.Product Weight: 7.2 lb (3.27 kg).Standards and CodesThe DS-DB Digital Series Distribution Board complies with the following standards:•NFP A 72 2007 National Fire Alarm Code •Underwriter Laboratories Standard UL 864•Underwriter Laboratories of Canada (ULC) ULC-S527-99Standard of Control Units for Fire Alarm Systems•Part 15 Class A conducted and radiated emissions as required by the FCC.•BC 2012, BC 2009, BC 2006, BC 2003, BC 2000(Seismic).•CBC 2007 (Seismic)Listings and ApprovalsThese listings and approvals apply to the DS-DB Digital Dis-tribution Board. In some cases, certain modules may not be listed by certain agencies, or listing may be in process. Con-sult factory for latest listing status.•UL Listed: S635.•ULC Listed: S635.•CSFM: 7165-0028:0243 (NFS2-640/NFS-320), 7165-0028:0224 (NFS2-3030).•Fire Dept. of New Y ork: COA#6121 (NFS2-640/NFS-320),COA#6114 (NFS2-3030).Product Line InformationDS-DB : Digital Series Distribution Board.DS-AMP: 120 VAC Digital Audio Amplifier (50/60 Hz), 125W (25V RMS ), 100W (70V RMS ). Ships with chassis. See DN-60663.DS-AMPE: 220-240 VAC Digital Audio Amplifier (50/60 Hz),125W (25V RMS ), 100W (70V RMS ). 70V RMS configuration requires step-up transformer. Ships with chassis. See DN-60663.DS-BDA: Backup amplifier, provides an economical means of backup for DS-AMP amplifiers in a one-to-one primary/backup configuration. Can also provide a second audio chan-nel for a DS-AMP when programmed as a primary amplifier.25V RMS or 70V RMS . 70V RMS configuration requires step-up transformer. See DN-60663.DS-XF70V: Step-up transformer, required for 70VRMS con-figuration of DS-AMP/E and DS-BDA. Ordered separately.See DN-60663.DS-FM: Fiber option module for multi-mode fiber. Converts a wire DAP (digital audio port) to a multi-mode fiber port. See DN-60633.DS-SFM: Fiber option module for single-mode fiber. Con-verts a wire DAP (digital audio port) to a single-mode fiber port. See DN-60633.DS-RFM: Fiber option module for multi-mode fiber. Used exclusively for compatibility with multi-mode fiber DVC or DAA. See DN-60633.。

SIMATIC Energy Manager PRO V7.2 - Operation Operat

SIMATIC Energy Manager PRO V7.2 - Operation Operat
Disclaimer of Liability We have reviewed the contents of this publication to ensure consistency with the hardware and software described. Since variance cannot be precluded entirely, we cannot guarantee full consistency. However, the information in this publication is reviewed regularly and any necessary corrections are included in subsequent editions.
2 Energy Manager PRO Client................................................................................................................. 19
2.1 2.1.1 2.1.2 2.1.3 2.1.4 2.1.5 2.1.5.1 2.1.5.2 2.1.6
Basics ................................................................................................................................ 19 Start Energy Manager ........................................................................................................ 19 Client as navigation tool..................................................................................................... 23 Basic configuration ............................................................................................................ 25 Search for object................................................................................................................ 31 Quicklinks.......................................................................................................................... 33 Create Quicklinks ............................................................................................................... 33 Editing Quicklinks .............................................................................................................. 35 Help .................................................................................................................................. 38

(3 封私信 _ 1 条消息) 地震可以预测吗? - 知乎

(3 封私信 _ 1 条消息) 地震可以预测吗? - 知乎

持这种观点的人的一个重要依据就是古登堡-里克特定律(Gutenberg-Richter law,指不同震级的地震 的频度与震级间存在某种对数关系),这一地震学中的重要定律的幂律分布性质是具有自组织临界性 系统中的普遍现象。
在地震的案例中,地球岩石层可以看作由不同层次的块体组成的,其中最大的是板块,最小的是矿物颗 粒。不同的块体之间、不同层次的块体之间都存在非线性的相互作用。地幔对流给这种块体运动提供 了持续的能量供给。这样一个动力系统能自发地演化到自组织临界状态。在1997年《科学》关于“地震 能否预测”的辩论中采取激进反对立场的罗伯特·盖勒(Robert J. Geller)就认为:“地球处在一种自组织 临界状态上,其中任何小地震都有可能级联式地发展成一个大地震。”因此,他当时的观点是,地震的 不可预测性是由这个系统本身的性质决定的,应当干脆放弃,不去研究它。
2014年5月17日
(3 封私信 / 1 条消息) 地震可以预测吗? - 知乎
地震 地震预测
地震可以预测吗?
看到国内很多民间预测者做了一些努力,想要问的是,国家有没有能力预测,国外在这方面的发展又 如何?有多少成功预测的案例?
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猪小宝,博士僧 | 一级注册结构工程师 148 刘学浩、诺兰、张潮勋 等人赞同
既然地震无法预测,那该怎么办?难道坐以待毙吗?
对 于 火 灾 , 没 有 人 说 要 " 预 测 火 灾 " 。 大家都知道合理的方法是用防火材料盖房子、保证疏散通 道、掌握合理逃生方法。地 震 也 是 一 样 ,提高结构抗震能力、做好物资储备这些紧急预案,就可以 了。纠结于地震预测,没有任何意义。与 其 嚷 嚷 着 要 预 测 地 震 , 不 如 踏 踏 实 实 把 房 子 修 结 实 点 , 把道路、水电、通信这些生命线工程做好一点,尽量让地震来时的损失小一点。

基于智能网格的灞桥区温度预报检验与订正

基于智能网格的灞桥区温度预报检验与订正

农业灾害研究 2023,13(7)基于智能网格的灞桥区温度预报检验与订正王一格1,刘延莉2,张宏利2,雷 宇21.西安市气象台,陕西西安 710016;2.灞桥区气象局,陕西西安 710038摘要 基于灞桥区12个区域自动站数据,采用6种滚动订正法,对陕西智能网格气象预报系统DCOEF的2 m气温预报产品进行误差订正。

结果表明,6种滚动订正法对DCOEF的2 m气温预报有不同程度订正效果:滑动误差回归订正效果最优,误差回归法和滑动双因子回归法次之;随预报时效延长,滑动双因子回归法较误差回归法和滑动误差回归法稳定性更好。

关键词 预报检验;偏差订正;格点温度预报中图分类号:P457.3 文献标识码:B 文章编号:2095–3305(2023)07–0117-03随着经济、社会高速发展,站点预报已经无法满足精细预报的需要,针对格点的精细化要素预报成为未来发展方向。

由于模式本身在动力框架、参数化过程和方案、初始场等方面存在误差,导致相应的模式预报产品存在不确定性,近年来随着模式不断发展,模式产品的误差在不断减小,但仍未完全消除。

在此背景下,对模式产品进行订正释用是提高气温客观预报准确率的重要手段,众多学者利用线性或非线性方法对模式预报产品进行订正。

白永清等[1]分时建立MOS方程能够有效降低夜间的预报误差,将实况最高气温引入卡尔曼滤波方程,提高预报准确率。

王婧等[2]在对比多种订正方法后认为采用滑动双权重平均法对2 m温度的预报订正效果最好。

精细化网格预报是中国气象局的主推业务和未来发展方向,陕西省气象局就此现状利用“动态交叉最优要素预报”(DCOEF)建立陕西省基础网格预报场,为网格预报发展提供参考[3]。

在此基础上,王瑾婷等[4]根据咸阳大风天气特征对大风预报产品进行订正。

本研究利用灞桥区12个气象观测站逐3 h数据,采用6种不同误差订正方法,对陕西智能网格气象预报系统(秦智)3 h预报时效2 m气温DCOEF预报产品进行误差订正,并对订正前后的预报结果检验分析,以期为灞桥区2 m气温预报提供参考。

unconstraint forecast实例

unconstraint forecast实例

unconstraint forecast实例无约束预测(Unconstrained Forecast)指的是在没有任何限制或条件下进行的预测分析。

这种预测方法不考虑任何限制因素,将数据直接拟合到最佳的预测模型上。

下面将通过一个实例来说明无约束预测的应用。

假设我们要对某个城市未来5年的人口增长进行预测,我们可以通过市政府的历史数据来进行分析和预测。

我们收集了过去20年的人口数据,包括每年的人口数量,以及其他一些可能与人口增长有关的因素,比如GDP增长、就业率等。

首先,我们需要对数据进行预处理,包括数据清洗、缺失值填充、异常值处理等。

然后,我们可以通过回归分析来探索人口增长与其他因素之间的关系。

可以使用线性回归、多项式回归、决策树等机器学习算法来建立预测模型。

假设我们使用多项式回归算法进行建模。

首先,我们需要选择合适的多项式阶数来拟合数据。

可以通过交叉验证等方法来确定最佳阶数。

然后,我们可以使用历史数据来训练模型,并使用交叉验证进行模型评估,以确定预测模型的准确性和稳定性。

完成模型训练和评估后,我们可以使用已训练的模型来进行未来人口增长的预测。

通过输入未来几年的GDP增长、就业率等因素的预估值,我们可以得到未来几年的人口增长预测结果。

无约束预测的优势在于其灵活性和简便性。

由于没有任何限制,我们可以将数据直接拟合到最佳的预测模型上,从而得到更准确和可靠的预测结果。

然而,无约束预测也存在一些局限性,比如对数据质量和特征选择的依赖较大,需要保证数据的准确性和完整性,并且需要选择合适的特征变量来进行建模。

总之,无约束预测是一种灵活、简便且有效的预测方法,可以应用于各种领域的预测分析。

然而,在使用无约束预测方法时,我们仍然需要谨慎选择数据和特征,并进行适当的预处理和模型评估,以确保预测结果的准确性和可靠性。

forecast的用法及固定搭配

forecast的用法及固定搭配

预测在我们日常生活中扮演着重要的角色,无论是个人规划还是商业决策,都需要对未来进行一定程度的预测。

在英语中,“forecast”是一个常用的动词,我们可以用它来表示预测未来的事物或者事件。

除了常规的用法外,它还有一些固定搭配,通过这些固定搭配,我们可以更准确地表达预测的内容和方式。

我们来看看“forecast”这个词在英语中的基本用法。

作为动词,“forecast”通常意味着预测或者预告。

在日常生活中,我们经常可以听到一些天气预报员使用这个词来预测未来的天气情况。

在商业和经济领域,“forecast”也被广泛使用,用来表示对未来市场走势、销售额等方面的预测。

我们可以看到,“forecast”这个词在不同的领域中都有着广泛的应用。

我们来看看“forecast”这个词在固定搭配中的运用。

在英语中,“forecast”常常和其他词语搭配在一起,形成一些固定的短语,来表示不同的预测情况。

“weather forecast”就是天气预报的意思,通过预测未来天气的变化,人们可以提前做好准备。

“economic forecast”则表示经济预测,这对政府决策和商业规划都具有重要意义。

除了这些常见的固定搭配外,“forecast”还可以和不同的名词组合,来形成更加专业和具体的预测内容。

在个人看来,预测具有一定的风险和不确定性,但在适当的情况下,它可以帮助我们做出更加明智的决策。

通过预测未来的趋势和发展,我们可以更好地规划自己的生活和工作。

作为一名文章写手,我认为对于“forecast”这个主题的深入理解和分析,可以帮助我们更好地把握未来的方向,从而提升我们的写作质量和深度。

总结而言,“forecast”作为一个动词,在英语中有着广泛的应用和丰富的固定搭配。

通过对这些固定搭配的学习和掌握,我们可以更加准确地表达自己对未来的预测和观点。

预测是一项具有挑战性的任务,但只有通过不断的学习和实践,我们才能够提升自己的预测能力和准确性。

时间序列预测之AUTO-ARIMA

时间序列预测之AUTO-ARIMA

时间序列预测之AUTO-ARIMA参考链接:运⽤ARIMA进⾏时间序列建模的基本步骤:1)加载数据:构建模型的第⼀步当然是加载数据集。

2)预处理:根据数据集定义预处理步骤。

包括创建时间戳、⽇期/时间列转换为d类型、序列单变量化等。

3)序列平稳化:为了满⾜假设,应确保序列平稳。

这包括检查序列的平稳性和执⾏所需的转换。

4)确定d值:为了使序列平稳,执⾏差分操作的次数将确定为d值。

5)创建ACF和PACF图:这是ARIMA实现中最重要的⼀步。

⽤ACF PACF图来确定ARIMA模型的输⼊参数。

6)确定p值和q值:从上⼀步的ACF和PACF图中读取p和q的值。

7)拟合ARIMA模型:利⽤我们从前⾯步骤中计算出来的数据和参数值,拟合ARIMA模型。

8)在验证集上进⾏预测:预测未来的值。

9)计算RMSE:通过检查RMSE值来检查模型的性能,⽤验证集上的预测值和实际值检查RMSE值。

ARMA模型公式:信息准则定阶AIC(Akaike Information Criterion)L是数据的似然函数,k=1表⽰模型考虑常数c,k=0表⽰不考虑。

最后⼀个1表⽰算上误差项,所以其实第⼆项就是2乘以参数个数。

AICc(修正过的AIC)BIC(Bayesian Information Criterion)注意事项:信息准则越⼩,说明参数的选择越好,⼀般使⽤AICc或者BIC。

差分d,不要使⽤信息准则来判断,因为差分会改变了似然函数使⽤的数据,使得信息准则的⽐较失去意义,所以通常⽤别的⽅法先选择出合适的d。

信息准则的好处是可以在⽤模型给出预测之前,就对模型的超参做⼀个量化评估,这对批量预测的场景尤其有⽤,因为批量预测往往需要在程序执⾏过程中⾃动定阶。

数据平稳性检验# 测试序列平稳性from statsmodels.tsa.stattools import adfullerdef test_stationarity(timeseries):#Determing rolling statisticsrolmean = timeseries.rolling(window=12).mean()rolstd = timeseries.rolling(window=12).std()#Plot rolling statistics:fig = plt.figure(figsize=(12, 8))orig = plt.plot(timeseries, color='blue',label='Original')mean = plt.plot(rolmean, color='red', label='Rolling Mean')std = plt.plot(rolstd, color='black', label = 'Rolling Std')plt.legend(loc='best')plt.title('Rolling Mean & Standard Deviation')plt.show()#Perform Dickey-Fuller test:#迪基-福勒检验还可以扩展为增⼴迪基-福勒检验(Augmented Dickey-Fuller test),简称ADF检验,可检验模型是否存在单位根(unit root)print('Results of Dickey-Fuller Test:')dftest = adfuller(timeseries, autolag='AIC')dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])for key,value in dftest[4].items():dfoutput['Critical Value (%s)'%key] = valueprint(dfoutput)要将⾮平稳时间序列转为平稳序列,有如下⼏种⽅法:Deflation by CPILogarithmic(取对数)First Difference(⼀阶差分)Seasonal Difference(季节差分)Seasonal Adjustment这⾥会尝试取对数、⼀阶查分、季节差分三种⽅法,先进⾏⼀阶差分,去除增长趋势后检测稳定性:可以看到图形上看上去变稳定了,但p-value的并没有⼩于0.05。

HAR模型包:自动回归模型的估计、模拟和预测说明书

HAR模型包:自动回归模型的估计、模拟和预测说明书

Package‘HARModel’October12,2022Type PackageTitle Heterogeneous Autoregressive ModelsVersion1.0Date2019-08-30Author Emil SjoerupMaintainer Emil Sjoerup<*******************>Description Estimation,simulation,and forecasting us-ing the HAR model from Corsi(2009)<DOI:10.1093/jjfinec/nbp001>and extensions. BugReports https:///emilsjoerup/HARModel/issuesURL https:///emilsjoerup/HARModelLicense GPL-3Imports Rcpp(>=0.12.17),xts,zoo,sandwichLinkingTo Rcpp,RcppArmadilloNeedsCompilation yesDepends R(>=2.10),methodsSuggests testthatRepository CRANDate/Publication2019-08-3111:30:02UTCR topics documented:HARModel-package (2)DJIRM (3)HAREstimate (3)HARForecast (6)HARForecast-class (9)HARModel-class (10)HARSim-class (11)HARSimulate (11)SP500RM (12)Index1312HARModel-package HARModel-package Heterogeneous Autoregressive ModelsDescriptionEstimation,simulation,and forecasting using the HAR model from Corsi(2009)<DOI:10.1093/jjfinec/nbp001>and extensions.DetailsThe DESCRIPTIONfile:Package:HARModelType:PackageTitle:Heterogeneous Autoregressive ModelsVersion: 1.0Date:2019-08-30Author:Emil SjoerupMaintainer:Emil Sjoerup<*******************>Description:Estimation,simulation,and forecasting using the HAR model from Corsi(2009)<DOI:10.1093/jjfinec/n BugReports:https:///emilsjoerup/HARModel/issuesURL:https:///emilsjoerup/HARModelLicense:GPL-3Imports:Rcpp(>=0.12.17),xts,zoo,sandwichLinkingTo:Rcpp,RcppArmadilloNeedsCompilation:YesDepends:R(>=2.10),methodsSuggests:testthatIndex of help topics:DJIRM Dow Jones Realized MeasuresHAREstimate HAR estimationHARForecast HAR forecastingHARForecast-class HARForecastHARModel-class HARModelHARModel-package Heterogeneous Autoregressive ModelsHARSim-class HARSimHARSimulate HAR simulationSP500RM SP500Realized MeasuresAuthor(s)Emil SjoerupMaintainer:Emil Sjoerup<*******************>DJIRM3 ReferencesCorsi,F.2009,A Simple Approximate Long-Memory Model of Realized V olatility,Journal of Fi-nancial Econometrics,174–196.DJIRM Dow Jones Realized MeasuresDescriptionRealized measures for the Dow Jones Industial index from2001to september2018FormatA large xts objectDetailsSee the website of the data set for details.Sourcehttps:///dataReferencesHeber,Gerd,Asger Lunde,Neil Shephard and Kevin Sheppard(2009)"Oxford-Man Institute’s realized library",Oxford-Man Institute,University of Oxford.Library version:0.3HAREstimate HAR estimationDescriptionHAR estimationUsageHAREstimate(RM,BPV=NULL,RQ=NULL,periods=c(1,5,22),periodsJ=NULL,periodsRQ=NULL,type="HAR",insanityFilter=TRUE,h=1)ArgumentsRM A numeric containing a realized measure of the integrated volatility.BPV A numeric containing the estimate of the continuous part of the integrated volatility used for HARJ and HARQ-J types.RQ A numeric containing the realized quarticity used for HARQ and HARQ-J types.periods A numeric denoting which lags should be used in the estimation,standard of c(1,5,22)is in line with Corsi(2009).periodsJ A numeric denoting which lags should be used in Jump estimation,if applica-ble.periodsRQ A numeric denoting which lags should be used in Realized Quarticity estima-tion,if applicable.type A character denoting which type of HAR model to estimate.insanityFilter A logical denoting whether the insanityfilter should be used for thefitted values of the estimation see Bollerslev,Patton&Quaedvlieg(2016)footnote17.h A integer denoting the whether and how much to aggregate the realized vari-ance estimator,if h=5the model is for the weekly volatility and if h=22,themodel is for the monthly volatility,the default of1designates no aggregation. DetailsThe estimates for the HARQ and HARQ-J models differ slightly from the results of BPQ(2016).This is due to a small difference in the demeaning approach for the realized quarticity.Here,the demeaning is done with mean(RQ)over all periods.ValueA HARModel objectAuthor(s)Emil SjoerupReferencesCorsi,F.2009,A Simple Approximate Long-Memory Model of Realized V olatility,Journal of Fi-nancial Econometrics,174–196.Bollerslev,T.,Patton,A.,Quaedvlieg,R.2016,Exploiting the errors:A simple approach for im-proved volatility forecasting,Journal of Econometrics,vol.192,issue1,1-18.Examples#Vanilla HAR from Corsi(2009)#load datadata("SP500RM")SP500rv=SP500RM$RV#Estimate the HAR model:FitHAR=HAREstimate(RM=SP500rv,periods=c(1,5,22))#extract the estimated coefficients:coef(FitHAR)#plot the fitted valuesplot(FitHAR)#calculate the Q-like loss-function:mean(qlike(FitHAR))#HAR-J:#load datadata("SP500RM")SP500rv=SP500RM$RVSP500bpv=SP500RM$BPV#Estimate the HAR-J model:FitHARJ=HAREstimate(RM=SP500rv,BPV=SP500bpv,periods=c(1,5,22),periodsJ=c(1,5,22),type="HARJ") #Calculate the Q-like loss-function:mean(qlike(FitHARJ))#HAR-Q of BPQ(2016)with weekly aggregation#load datadata("SP500RM")SP500rv=SP500RM$RVSP500rq=SP500RM$RQ#Estimate the HAR-Q model:FitHARQ=HAREstimate(RM=SP500rv,RQ=SP500rq,periods=c(1,5,22),periodsRQ=c(1,5,22),type="HARQ",h=5)#Show the model:show(FitHARQ)#Extract the coefficients:HARQcoef=coef(FitHARQ)#HARQ-J of BPQ(2016)with monthly aggregation#load datadata("SP500RM")SP500rv=SP500RM$RVSP500rq=SP500RM$RQSP500bpv=SP500RM$BPV#Estimate the HARQ-J model:FitHARQJ=HAREstimate(RM=SP500rv,BPV=SP500bpv,RQ=SP500rq,periods=c(1,5,22),periodsJ=c(1),periodsRQ=c(1),type="HARQ-J",h=22)#show the model:show(FitHARQJ)HARForecast HAR forecastingDescriptionRolling out of sample forecasting of a HAR model.UsageHARForecast(RM,BPV=NULL,RQ=NULL,periods=c(1,5,22),periodsJ=NULL,periodsRQ=NULL,nRoll=10,nAhead=1,type="HAR",windowType="rolling",insanityFilter=TRUE,h=1)ArgumentsRM An xts object containing a realized measure of the integrated volatility.BPV A numeric containing the jump proportion of the realized measure used for HARJ and HARQ-J types.RQ A numeric containing the realized quarticity used for HARQ and HARQ-J types.periods A vector denoting which lags should be used in the estimation,standard of c(1,5,22)is in line with Corsi(2009).periodsJ A numeric denoting which lags should be used in Jump estimation,if applica-ble.periodsRQ A numeric denoting which lags should be used in Realized Quarticity estima-tion,if applicable.nRoll How many rolling forecasts should be performed.nAhead The length of each rolling forecast.type A character denoting which type of HAR model to estimate.windowType A character denoting which kind of window to use,either"rolling"/"fixed"or "increasing"/"expanding".2-letter abbreviations can be used.insanityFilter A logical denoting whether the insanityfilter should be used for the forecasted values see Bollerslev,Patton&Quaedvlieg(2016)footnote17.h A integer denoting the whether and how much to aggregate the realized vari-ance estimator,if h=5the model is forecasting the weekly volatility and if h=22,the model is forecasting the monthly volatility,the default of1designatesno aggregation..DetailsNot all models in this package are’complete’,which means some models use AR(1)processes to forecast e.g.realized quarticity in order to construct more than one step ahead forecasts.The maximumm lag of the continuous or quarticity data must be lower than the maximum of the realized measure lag vector,the other cases are not implemented.The estimates for the HARQ and HARQ-J models differ slightly from the results of BPQ(2016).This is due to a small difference in the demeaning approach for the realized quarticity.Here,the demeaning is done with mean(RQ)over all periods.If h is greater than1,then nAhead must be one,as multi-period ahead forecasts have not been implemented.ValueA HARForecast objectAuthor(s)Emil SjoerupReferencesCorsi,F.2009,A Simple Approximate Long-Memory Model of Realized V olatility,Journal of Fi-nancial Econometrics,174–196.Bollerslev,T.,Patton,A.,Quaedvlieg,R.2016,Exploiting the errors:A simple approach for im-proved volatility forecasting,Journal of Econometrics,vol.192,issue1,1-18.See AlsoSee Also HAREstimateExamples#HAR of Corsi(2009)#load data:data("SP500RM")SP500rv=SP500RM$RVForecastHAR=HARForecast(SP500rv,periods=c(1,5,22),nRoll=50,nAhead=50,type="HAR")#plot the forecasted series along with the actual realizations:plot(ForecastHAR)#Calculate the MSE:mean(forecastRes(ForecastHAR)^2)#Calculate the Q-like loss function:mean(qlike(ForecastHAR))#HARJ#load data:data("SP500RM")SP500rv=SP500RM$RVSP500bpv=SP500RM$BPVForecastHARJ=HARForecast(SP500rv,BPV=SP500bpv,periods=c(1,5,22),periodsJ=c(1,5,22),nRoll=50,nAhead=50,type="HARJ")#Show the model:show(ForecastHARJ)#Extract the forecasted series:forc=getForc(ForecastHARJ)#HARQ BPQ(2016)#load datadata("SP500RM")SP500rv=SP500RM$RVSP500rq=SP500RM$RQForecastHARQ=HARForecast(SP500rv,RQ=SP500rq,periods=c(1,5,22),periodsRQ=c(1,5,22),nRoll=50,nAhead=50,type="HARQ")#HARQ-J BPQ(2016)with weekly aggregation.#load datadata("SP500RM")SP500rv=SP500RM$RVSP500rq=SP500RM$RQSP500bpv=SP500RM$BPVForecastHARQJ=HARForecast(SP500rv,RQ=SP500rq,BPV=SP500bpv,periods=c(1,5,22),periodsJ=c(1,5,22),periodsRQ=c(1,5,22),nRoll=50,nAhead=1,type="HARQ-J",h=5)HARForecast-class9 HARForecast-class HARForecastDescriptionClass for HARForecast objectObjects from the ClassA virtual Class:No objects may be created from itSlotsmodel:Object of class HARModel.see HARModelforecast:Object of class matrix containing the forecasted seriesinfo:Object of class list cointaining:•elapsedTime:Object of class difftime containing the time elapsed in seconds•rolls:Integer containing the amount of rolls done in the forecasting routine•horizon:Integer containing the length of the horizon used for forecasting during eachof the rollsdata:Object of class list containing:•dates:Object of type Integer or Date containing the indices of the forecasted serieseither in integer or date format•observations:Object of type numeric or xts containing the in-sample observations•forecastComparison:Object of type numeric or xts containing the observations keptout of sample for thefirst rollMethodsshow:signature(object="HARForecast"):Shows summaryplot:signature(x="HARForecast",y="missing"):Plot the out of sample observed series with the forecasts overlayeduncmean:signature(object="HARForecast"):Extracts the unconditional mean from the Model coef:signature(object="HARForecast"):Extracts the coefficients from thefirst estimated Modelqlike:signature(object="HARForecast"):Calculate the out of sample’qlike’loss function for a HARForecast objectforecastres:signature(object="HARForecast"):Retrieve the forecast residuals from HAR-Forecast objectforc:signature(object="HARForecast"):Retrieve the forecasted series.Author(s)Emil Sjoerup10HARModel-class HARModel-class HARModelDescriptionClass for HARModel objectsObjects from the ClassA virtual Class:No objects may be created from it.Slotsmodel:Object of class lm.Contains the linear modelfitted.info:Object of class list cointaining:•periods:numeric containing the lags used to create the model.If the type isn’t"HAR",then the related periods-(RQ)and/or(J)will also be included.•dates:Date object containing the dates for which the estimation was done,only appli-cable if the Model was estimated using an"xts"object.Methodsshow:signature(object="HARModel")Shows summaryplot:signature(x="HARModel",y="missing"):Plots the observed values withfitted values overlayeduncmean:signature(object="HARModel"):Extracts the unconditional mean from the Model, only available when type="HAR"coef:signature(object="HARModel"):Extracts the coefficients from the ModelsandwichNeweyWest:signature(object="HARModel"):Utilize the sandwich package to cre-ate newey west standard errorsqlike:signature(object="HARModel"):Calculate the in sample’qlike’loss function for a HARModel objectlogLik:A wrapper for the"lm"subclass of the HARModel objectconfint:A wrapper for the"lm"subclass of the HARModel objectresiduals:A wrapper for the"lm"subclass of the HARModel objectsummary:A wrapper for the"lm"subclass of the HARModel objectAuthor(s)Emil SjoerupHARSim-class11 HARSim-class HARSimDescriptionClass for HARSim objectObjects from the ClassA virtual Class:No objects may be created from itSlotssimulation:Object of class numeric containing the simulated seriesinfo:Object of class list cointaining:•len:Object of class numeric containing the length of the simulated series•periods:Object of class numeric containing the lag-vector used for simulation•coefficients:Object of class numeric containing the coefficients used for simulation•errorTermSD:Object of class numeric containing the standard error of the error term•elapsedTime:Object of class difftime containing the time elapsed in secondsMethodsshow:signature(object="HARSim"):Shows summaryplot:signature(x="HARSim",y="missing"):Plot the forecasted series and observed series as well as the residualsuncmean:signature(object="HARSim"):Extracts the unconditional mean from the simulation coef:signature(object="HARSim"):Extracts the coefficients from the simulationAuthor(s)Emil SjoerupHARSimulate HAR simulationDescriptionSimulates a HAR model.From using the AR representation of the HAR model.UsageHARSimulate(len=1500,periods=c(1,5,22),coef=c(0.01,0.36,0.28,0.28),errorTermSD=0.001)12SP500RMArgumentslen An integer determining the length of the simulated process.periods A numeric of lags for constructing the model,standard is c(1,5,22).coef A numeric of coefficients which will be used to simulate the process.errorTermSD A numeric determining the standard deviation of the error term.ValueA HARSim objectAuthor(s)Emil SjoerupReferencesCorsi,F.2009,A Simple Approximate Long-Memory Model of Realized V olatility,Journal of Fi-nancial Econometrics,174–196.Examplesset.seed(123)#Simulate the process of size10000HARSim=HARSimulate(len=10000,periods=c(1,5,22),coef=c(0.01,0.36,0.28,0.28),errorTermSD=0.001) HARFit=HAREstimate(HARSim@simulation,periods=c(1,5,22))SP500RM SP500Realized MeasuresDescriptionRealized measures from the SP500index from April1997to August2013.FormatA large xts object.Source/~ap172/code.htmlReferencesBollerslev,T.,A.J.Patton,and R.Quaedvlieg,2016,Exploiting the Errors:A Simple Approach for Improved V olatility Forecasting,Journal of Econometrics,192,1-18.Index∗HARHARForecast,6HARSimulate,11∗Heterogeneous Autoregressive model HARModel-package,2∗classesHARForecast-class,9HARModel-class,10HARSim-class,11∗datasetsDJIRM,3SP500RM,12∗forecastHARForecast,6∗simulationHARSimulate,11coef,HARForecast-method(HARForecast-class),9coef,HARModel-method(HARModel-class), 10coef,HARSim-method(HARSim-class),11 confint,HARModel-method(HARModel-class),10DJIRM,3fitted.values,HARModel-method(HARModel-class),10 forecastRes(HARForecast-class),9 forecastRes,ANY-method(HARForecast-class),9 forecastRes,HARForecast-method(HARForecast-class),9getForc(HARForecast-class),9 getForc,ANY-method(HARForecast-class), 9getForc,HARForecast-method(HARForecast-class),9HAREstimate,3,7HARForecast,6,7HARForecast-class,9HARModel,4,9HARModel(HARModel-package),2HARModel-class,10HARModel-package,2HARSim,12HARSim-class,11HARSimulate,11logLik,HARModel-method(HARModel-class),10plot,HARForecast,missing-method(HARForecast-class),9plot,HARModel,missing-method(HARModel-class),10plot,HARSim,missing-method(HARSim-class),11qlike(HARModel-class),10qlike,ANY-method(HARModel-class),10qlike,HARForecast-method(HARForecast-class),9qlike,HARModel-method(HARModel-class),10residuals,HARModel-method(HARModel-class),10sandwichNeweyWest(HARModel-class),10sandwichNeweyWest,ANY-method(HARModel-class),10sandwichNeweyWest,HARModel-method(HARModel-class),10show,HARForecast-method(HARForecast-class),9show,HARModel-method(HARModel-class),10show,HARSim-method(HARSim-class),11 1314INDEX SP500RM,12summary,HARModel-method(HARModel-class),10uncmean(HARModel-class),10uncmean,ANY-method(HARModel-class),10uncmean,HARForecast-method(HARForecast-class),9uncmean,HARModel-method(HARModel-class),10uncmean,HARSim-method(HARSim-class),11。

关于天气的英语俚语

关于天气的英语俚语

关于天气的英语俚语[标签:标题]篇一:天气有关的英语词汇及句子与天气有关的英语词汇及句型天气是英国人迷恋的话题,英式英语中有许多与天气有关的英语成语,一起来看看吧!it never rains but it pours 祸不单行thrown caution to the wind 冒险a storm in teacup 大惊小怪twisting in the wind 孤立无援rainy day 艰难的时刻/困境ill wind that blows nobody any good 有祸也有福/有人愁有人喜every cloud has a silver lining (每一块乌云后面都有银色的一面)困难中必有希望bolt from the blue 突如其来的under the weather 身体不舒服weather the storm 渡过危机as right as rain 万事顺利跟天气有关的英语俚语1. What's up with him today? He has a face like thunder!他今天怎么了?他看着怒气冲冲的。

2. I'm a bit disappointed in John and David. It turned out they were only fair-weather friends.我对约翰和大卫有点失望,原来他们不过是靠不住的酒肉朋友。

3. We don't have a snowball's chance of winning that contract!我们根本就没有一点希望能签那份合同。

4. Don't worry about those two arguing. it's just a storm in a teacup.不用为他俩的争执担心,他们不过是小题大做罢了。

5. The exam was a breeze.这次考试真是太容易了。

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Budgeting and Forecasting with a rolling forecast
Forecasting like budgeting involves future projections. Traditionally businesses look at a static period for example 1 year ahead. If the financial year is July to June then budgets and forecasts are prepared well in advance to cover that period. By October, 3 months of trading has already taken place leaving only 9 months remaining of the forecast. With static forecasts you run the periods down to zero and then start again. With a rolling forecast the number of periods in the forecast remain constant so that if for example the periods of your forecast are monthly for 12 months then as each month is traded it drops out of the forecast and another month is added onto the end of the forecast so you are always forecasting 12 monthly periods out into the future.
The number of periods in a rolling forecast remains constant e.g. the original forecast if for 12 periods and the period length is monthly from July to June.
As actual trading amounts become available for a period it moves from being a future prediction to a current reality i.e. it is no longer part of the forecast so it drops out of the forecast and another month is added onto the end of the forecast (you roll the forecast forward one month) so you are always forecasting 12 months out into the future.
Benefits of rolling forecast.
In the modern world business conditions are volatile and change rapidly. There is little future for a business not in a position to respond quickly to external market changes so flexible planning processes are vital to rise to the challenge. This is where the effectiveness of rolling forecasts in the planning process are most keenly felt. With rolling forecasts you keep a finger on the pulse of changing conditions and can quickly refocus the business accordingly e.g. decisions on which projects to scrap and which to invest in can be made timeously.
Obviously the speed and accuracy of your financial forecasting will have a bearing on the profit of the organization. These factors can be ehanced by using the correct tool for the job. Like budgeting there are basically two approaches for rolling forecasts. You can either use a spreadsheet or some spreadsheet based solution with all its inherent risks and defects, or you can use dedicated solution like Visual Cash Focus budgeting and forecasting software. The huge advantage of a dedicated solution lies in the fact that it is not human resource reliant. Anyone who has been left to pick up the pieces of a spreadsheet started by someone else will be all to familiar with the perils inherent in trying to unravel the "masterpiece" of a colleague no longer available
for consultation. Also financial specialists generally have very different skill sets from those necessary for complex spreadsheet programming and its not the best use of their skills to waste time doing something outside their specialized skill set.
Annual budget rounds are resource intensive in time and money, need to be prepared well in advance of the planning periods under review and are often outdated from changing market conditions pretty early on in the time frame. With Visual Cash Focus the last period of the forecast is rolled forward as the basis for new period so the managers are always reviewing the most up to date predictions for the rolling forecasts. If your forecast periods are monthly and you roll the forecast 12 times then the following years forecast is complete without the need for another budget round so a rolling forecast can eliminate the annual budget process.
Probably the most important beneficiary of a good rolling forecast is having an accurate handle on likely future cash flows in the business. No longer is the income statement a sufficient measure of viability or being able to turn to banks for funding cash shortfalls a certainty. Since the global financial crisis bank credit is in very short supply and unless the business has its own cash reserves the road ahead is far from secure.
1最敏锐地感觉到重新确定相应的业务例如决定哪些项目取消,哪些投资可timeously。

2未来现金流量。

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