Time series Data
Time-Series Data AnalysisPPT教学课件
Exponential smoothing---a tool for noise filtering and forecasting
simple exponential smoothing--- the best
model for one-period-ahead forecasting
among 25 time series methods(Markridakis
et la )
2020/12/10
6
Statistical Methods- Exponential smoothing)
formula of the exponential smoothing:
Xt = b + t St = Xt + (1 - )St-1 Where
t: error b: constant
Time-Series Data Analysis
Statistical Methods Fuzzy Logic in Time-Series Data Analysis Chaos Theory in Time-Series Data Analysis Application of Artificial Neural Networks to Time-Series Data Analysis
Trend component --- a long-term change that does not repeat at the time range being considered Seasonal component(seasonality)--- regular fluctuations in systematic intervals. Noise (error)---irregular component
matlab中timeseries数据提取-概述说明以及解释
matlab中timeseries数据提取-概述说明以及解释1.引言1.1 概述概述：在现实生活中，我们经常需要对时间序列数据进行处理和分析。
时间序列数据是按照时间顺序排列的一系列观测值或数据点的集合。
在matlab 中，timeseries是一种强大的数据类型，用于存储和操作时间序列数据。
timeseries数据可以包含多个变量，每个变量都与时间相关联。
本文将介绍如何在matlab中提取timeseries数据，即从整个数据集中选择需要的部分。
这些部分可以是特定时间范围内的数据，或者是满足特定条件的数据点。
在本文的第二节中，我们将详细介绍timeseries数据的基本概念和特性。
我们将探讨如何创建和访问timeseries对象，以及如何处理多变量的timeseries数据。
在第三节中，我们将介绍几种常见的timeseries数据提取方法。
这些方法包括按时间范围提取数据、按条件提取数据以及按特定时间间隔提取数据等。
我们将通过具体的示例来说明这些方法的用法和效果。
在第四节中，我们将通过一些实际应用案例来展示timeseries数据提取的实际应用。
这些案例涵盖了金融数据分析、气象数据预测和生物医学信号处理等领域。
最后，在结论部分，我们将对本文进行总结，并展望timeseries数据提取在未来的发展前景。
本文的目的是帮助读者更好地理解和应用matlab 中的timeseries数据提取方法，以便能够更加高效地处理和分析时间序列数据。
通过本文的阅读，读者将能够掌握matlab中timeseries数据提取的基本方法和技巧，为自己的数据处理和分析工作提供有力的支持。
1.2 文章结构文章结构部分应包括以下内容：文章结构部分旨在向读者介绍本文的整体框架和内容安排。
通过明确的结构和组织，读者可以更好地理解文章的主要内容和各部分之间的关系。
本文的结构分为引言、正文和结论三个部分。
1. 引言部分位于文章开头，旨在引入读者对于本文所讨论的主题和背景有所了解。
折线统计图的英语作文
折线统计图的英语作文Line Graphs: A Visual Representation of Time-Series Data.In the realm of data visualization, line graphs emerge as a powerful tool for showcasing the evolution of data points over time. Their simple yet effective design allows viewers to quickly discern trends, patterns, and relationships within a time-series dataset.Structure and Elements of a Line Graph.A line graph comprises the following key elements:X-axis (Horizontal Axis): Represents the independent variable, typically representing time.Y-axis (Vertical Axis): Represents the dependent variable, indicating the data values being plotted.Data Points: Individual data values plotted on the graph as points or symbols (e.g., circles, squares, triangles).Line: Connects the data points to form a continuous line, depicting the trend of the data over time.Types of Line Graphs.Line graphs can be classified based on the nature of the data and the purpose of the visualization:Simple Line Graph: Represents a single time-series dataset.Multi-Series Line Graph: Compares multiple time-series datasets on the same graph.Stacked Line Graph: Visualizes the cumulative contribution of different categories or components within a single time-series dataset.Smoothed Line Graph: Uses mathematical techniques to smooth out fluctuations and highlight the overall trend of the data.Creating an Effective Line Graph.Effective line graphs adhere to the following principles:Clear and concise: Use a straightforward design with minimal distractions.Accurate and reliable: Ensure that the data points and line representation accurately reflect the underlying data.Appropriate scale: Choose a scale that allows for easy comparison and interpretation of the data values.Relevant labels: Provide clear labels for both axes and any additional elements on the graph.Meaningful colors: Use colors that differentiatebetween different data series or categories, enhancing readability.Contextual information: Include a title and any necessary annotations to provide context for the graph.Applications of Line Graphs.Line graphs find widespread use in various fields, including:Economics: Tracking economic indicators such as GDP, unemployment rates, and stock prices.Science: Visualizing the results of experiments, such as changes in temperature, concentration, or reaction rates over time.Healthcare: Monitoring patient data, such as temperature, heart rate, and medication effectiveness.Marketing: Analyzing sales trends, customer engagement,and campaign performance.Education: Illustrating historical events, scientific concepts, and the progression of knowledge.Advantages and Limitations of Line Graphs.Advantages:Easy to understand and interpret.Effective for visualizing trends and patterns over time.Comparatively less complex to create than other types of graphs.Limitations:Limited in their ability to show complex relationships or multiple dimensions of data.Can be misleading if the data points are too sparse or the time interval is too large.May not be suitable for datasets with high volatility or numerous data points.Conclusion.Line graphs remain a versatile and essential tool for visualizing and analyzing time-series data. Theirsimplicity and effectiveness make them accessible to a wide audience, allowing for the quick identification of trends and patterns. By adhering to best practices and considering the appropriate type of line graph, users can harness the power of line graphs to effectively communicate their data insights.。
pytorch-forecasting timeseriesdataset格式 -回复
pytorch-forecasting timeseriesdataset格式-回复pytorchforecasting timeseriesdataset格式PyTorchForecasting是一个用于时间序列预测的开源库，它建立在PyTorch深度学习框架之上，并提供了多种模型和方法来处理时间序列数据。
其中，TimeseriesDataset是该库中的一个关键类，用于处理和准备时间序列数据。
本文将详细讨论TimeseriesDataset的格式和用法，以帮助读者更好地理解和使用该类。
首先，让我们从TimeseriesDataset的基本概念开始。
TimeseriesDataset 是PyTorchForecasting中的一个数据集类，用于将时间序列数据转换为可供深度学习模型使用的格式。
它的主要目的是为了简化数据的准备和处理过程，以便在PyTorch中进行训练和预测。
TimeseriesDataset的格式要求如下：1. 时间索引：每个时间序列数据必须包含一个时间索引列，通常是日期或时间戳。
这个时间索引是数据在时间上的顺序，并且应当是递增的。
在构建TimeseriesDataset时，需要传入一个时间索引列的名称。
2. 静态特征：除了时间索引以外，时间序列数据还可以包含静态特征，这些特征在整个时间序列中保持不变。
例如，如果你正在预测销售量，静态特征可以是店铺的面积、位置等。
在构建TimeseriesDataset时，需要传入一个静态特征列表。
3. 动态特征：动态特征是随时间变化的特征，每个时间步都有不同的值。
例如，如果你正在预测销售量，动态特征可以是过去几天的销售数据。
在构建TimeseriesDataset时，需要传入一个动态特征列表。
除了以上三个主要要求，TimeseriesDataset还提供了其他可选参数和功能，以适应不同的时间序列预测任务。
比如，你可以指定训练集和测试集的时间范围、定义滑动窗口的大小、指定目标列等。
pytorch-forecasting timeseriesdataset格式 -回复
pytorch-forecasting timeseriesdataset格式-回复PyTorchForecasting是一个基于PyTorch的时间序列预测库，它提供了强大的功能和灵活的数据处理能力。
其中一个重要的数据结构是TimeseriesDataset，它是PyTorchForecasting用于处理时间序列数据的格式。
在本文中，我们将一步一步回答关于TimeseriesDataset的问题，并介绍如何使用它来处理时间序列数据。
第一部分：什么是TimeseriesDataset？TimeseriesDataset是PyTorchForecasting中的一个数据结构，用于管理和处理时间序列数据。
它是基于PyTorch的Dataset类的扩展，并提供了额外的功能来处理时间序列数据，例如对时间步长和目标时序进行索引、重采样和变换等。
第二部分：如何创建TimeseriesDataset？PyTorchForecasting提供了一个用于创建TimeseriesDataset的工具类TabularDataset，它可以根据不同的数据源构建TimeseriesDataset对象。
我们可以从CSV文件、Pandas DataFrame或直接从内存中的numpy数组中加载数据。
1. 从CSV文件加载数据使用TabularDataset.from_csv方法可以从CSV文件中加载数据。
需要提供CSV文件的路径、时间索引列的名称以及其中包含的输入变量和目标变量的名称。
可以选择性地指定其他参数，例如转换函数和缺失值处理方法。
2. 从Pandas DataFrame加载数据使用TabularDataset.from_data_frame方法可以基于Pandas DataFrame加载数据。
需要提供一个包含输入变量和目标变量的DataFrame，以及时间索引列的名称。
同样，可以选择性地指定其他参数以进行数据转换和处理。
TIME-SERIES DATA STORAGE AND PROCESSING DATABASE S
专利名称：TIME-SERIES DATA STORAGE AND PROCESSING DATABASE SYSTEM 发明人：Tobin, David,Scott, Dylan,Orcun,Simsek,Fackler, Steven,Wong, Wilson 申请号：EP16173056.9申请日：20160606公开号：EP3101560B1公开日：20210519专利内容由知识产权出版社提供摘要：A database system is described that includes components for storing time-series data and executing custom, user-defined computational expressions in substantially real-time such that the results can be provided to a user device for display in an interactive user interface. For example, the database system may process stored time-series data in response to requests from a user device. The request may include a start time, an end time, a period, and/or a computational expression. The database system may retrieve the time-series data identified by the computational expression and, for each period, perform the arithmetic operation(s) identified by the computational expression on data values corresponding to times within the start time and the end time. Once all new data values have been generated, the database system may transmit the new data values to the user device for display in the interactive user interface.代理机构：Piotrowicz, Pawel Jan Andrzej更多信息请下载全文后查看。
时间序列数据的标准化
时间序列数据的标准化Time series data refers to a series of data points indexed in time order. This type of data is commonly seen in various fields, such as finance, economics, weather forecasting, and more. However, raw time series data can be quite noisy and have varying scales, making it challenging to analyze effectively. One common way to address this issue is by standardizing or normalizing the data.时间序列数据指的是按时间顺序索引的一系列数据点。
这种类型的数据在各个领域中很常见，比如金融、经济、天气预报等。
然而，原始的时间序列数据可能会很嘈杂，且具有不同的尺度，这使得分析变得困难。
解决这个问题的一种常见方法是对数据进行标准化或归一化。
Standardization of time series data involves transforming the data values so that they have a mean of zero and a standard deviation of one. This process helps in bringing all the data points to a common scale, making it easier to compare and analyze them. By standardizing the data, we can remove the effects of differing scales and focus on the patterns and trends within the data.时间序列数据的标准化涉及将数据值转换为均值为零、标准偏差为一的形式。
关于时间演变的资料
关于时间演变的资料英文回答：What are the different types of time-series data?Time-series data can be classified into several different types based on various characteristics such as data format, time intervals, and underlying patterns. Here are some of the most common types of time-series data:Continuous data: This type of data consists of continuous measurements taken over time, typically at regular intervals. Examples include temperature readings from a weather station or stock prices recorded every minute.Discrete data: Unlike continuous data, discrete data comprises individual observations that occur at specific time points. For instance, the number of website visits per hour or the daily sales figures of a retail store.Univariate data: Univariate time-series data involves a single variable measured over time. It represents the evolution of a single value, such as the daily temperature or the monthly sales volume.Multivariate data: Multivariate time-series data consists of multiple variables measured simultaneously over time. It captures the relationships and interdependencies among different variables. An example would be a dataset containing daily weather conditions, including temperature, humidity, wind speed, and rainfall.Equally spaced data: This type of data has observations taken at regular, equally spaced time intervals. For example, hourly temperature readings ordaily stock prices.Unequally spaced data: In contrast to equally spaced data, unequally spaced data has observations taken at irregular, varying time intervals. This can happen when measurements are taken manually or when events occurrandomly.Stationary data: Stationary time-series data exhibits constant statistical properties over time. The mean, variance, and autocorrelation of the data remain relatively unchanged.Non-stationary data: Non-stationary time-series data has statistical properties that vary over time. The mean, variance, or autocorrelation can change over time, indicating a trend, seasonality, or other patterns.Deterministic data: Deterministic time-series data is predictable based on known mathematical equations or functions. The future values of the data can be precisely calculated using these equations.Stochastic data: Stochastic time-series data is random and unpredictable. Its future values cannot be accurately predicted using deterministic models. Instead,probabilistic models are used to estimate future values.What are the challenges in forecasting time-series data?Forecasting time-series data presents severalchallenges due to the complexities and uncertaintiesinherent in real-world data. Some of the key challenges include:Non-linear patterns: Time-series data often exhibits non-linear patterns, making it difficult to model andpredict accurately.Seasonality and trends: Many time-series datasets exhibit seasonality (e.g., daily, weekly, or yearly patterns) or long-term trends, which need to be accountedfor in forecasting models.Missing data and outliers: Missing values and outliers can disrupt time-series analysis and forecasting. Missing data can lead to biased estimates, while outliers candistort the model's predictions.Overfitting and underfitting: Forecasting models cansuffer from overfitting or underfitting. Overfitting occurs when a model is too complex and captures noise instead of true patterns, leading to inaccurate predictions. Underfitting, on the other hand, occurs when a model is too simple and fails to capture important patterns in the data.Data limitations: The availability of historical data and the length of the time series can limit the accuracyand reliability of forecasts. Insufficient data can make it challenging to identify patterns and predict future trends.中文回答：时间序列数据的不同类型是什么？时间序列数据可以根据各种特征（例如数据格式、时间间隔和内在模式）分为几种不同的类型。
平稳时间序列英文缩写
平稳时间序列英文缩写When dealing with time series data, it's crucial to identify whether the series is stationary or not. Stationary time series, often abbreviated as STS, refer to those series whose statistical properties like mean, variance, and autocovariance remain constant over time.In the world of data analysis, a stationary time series is like a calm lake. It doesn't have big waves or unexpected fluctuations, but it remains stable and predictable. And when you want to model or forecast such series, STS becomes a handy abbreviation to refer to.For economists and financial analysts, understanding STS is key. It helps them identify patterns and trends in economic indicators or stock prices without worrying about seasonal effects or long-term trends. In simple terms, it's a time series that's "behaving itself" and not showing any strange fluctuations.But let's not get too technical here. Imagine you're measuring the temperature of a city over a year. If the temperature doesn't show any significant changes from one month to another, we can say it's a stationary time series. STS, in this case, is just a shorthand for "consistent temperature readings."So, whether you're a data scientist, an economist, or just curious about how the world works, remember that STS stands for stationary time series and it's all about stability and predictability in your data.。
时间序列与arima模型的关系
英文回复：Time—series data are observations or records over time that are important for analysing and predicting future trends，cyclicality and regularity。
As amon statistical method， time—series analysis is aimed at effectively predicting future developments through in—depth analysis of historical data。
For time series analysis， there are many models and methods，of which the ARIMA model is an effective one。
At this critical point， we should pursue an approach that closely integrates practical， integrated and scientific decision—making and promotes continuous innovation in time—series analysis theory and methodology to better serve our countries and peoples。
时间序列数据是指各种数据随着时间的推移所呈现出的观测结果或记录，其对于分析和预测未来的趋势、周期性和规律性具有重要意义。
时间序列分析作为一种常见的统计方法，通过对历史数据的深入剖析，旨在有效预测未来的发展走势。
针对时间序列分析，存在多种模型和方法，其中ARIMA模型为行之有效的一种。
值此关键节点，我们应坚持紧密结合实际、统筹兼顾、科学决策的方针，推动时间序列分析理论与方法的不断创新，以更好地服务于我们的国家和人民大众。
matlab 时间序列timeseries转数组
在 MATLAB 中，将timeseries对象转换为数组通常涉及提取时间和数据。
以下是一个简单的示例，演示如何从timeseries对象中提取时间和数据，并将它们存储在数组中：
在这个示例中：
1.创建了一个包含随机数据的timeseries对象。
2.使用ts.Time和ts.Data提取时间和数据。
3.将时间转换为datetime格式。
4.将数据转换为列向量。
请注意，ts.Time返回的时间可能是 MATLAB 的内部表示方式，是以自公元0年1月1日午夜开始的天数表示的。
因此，我们使用datetime函数将其转换为更常见的datetime格式。
这只是一个简单的示例，实际情况中，你可能需要根据你的数据和需求进行适当的处理。
你可以根据具体情况对时间和数据进行额外的处理或分析。
matlab中timeseries函数
matlab中timeseries函数MATLAB中的timeseries函数是一种用于处理时间序列数据的强大工具。
它可以帮助用户轻松地创建、操作和可视化时间序列数据，从而更好地理解和分析数据。
timeseries函数的基本语法如下：ts = timeseries(data, time)其中，data是一个向量或矩阵，表示时间序列数据；time是一个向量或矩阵，表示时间序列数据对应的时间点。
ts是一个timeseries对象，包含了data和time两个属性。
使用timeseries函数可以进行多种操作，例如：1. 创建时间序列数据可以使用timeseries函数创建一个新的时间序列数据对象。
例如，以下代码创建了一个包含10个随机数的时间序列数据对象：data = rand(10,1);time = 1:10;ts = timeseries(data, time);2. 访问时间序列数据可以使用timeseries对象的Data和Time属性来访问时间序列数据。
例如，以下代码访问了ts对象的Data和Time属性，并将它们打印出来：disp(ts.Data);disp(ts.Time);3. 修改时间序列数据可以使用timeseries对象的Data和Time属性来修改时间序列数据。
例如，以下代码将ts对象的第一个数据点修改为0：ts.Data(1) = 0;4. 可视化时间序列数据可以使用timeseries对象的plot函数来可视化时间序列数据。
例如，以下代码绘制了ts对象的折线图：plot(ts);5. 时间序列数据的运算可以使用timeseries对象进行时间序列数据的运算。
例如，以下代码将ts对象的数据点加上1：ts.Data = ts.Data + 1;总之，timeseries函数是MATLAB中非常有用的一个函数，它可以帮助用户轻松地创建、操作和可视化时间序列数据。
通过使用timeseries函数，用户可以更好地理解和分析数据，从而做出更好的决策。
timeserial 用法
timeserial 用法timeserial是一种用于对一组数据进行排序的算法，它可以将一组数据按照时间顺序进行排序，以便于对数据进行管理和分析。
在本文中，我们将介绍timeserial的基本概念、使用方法、示例和注意事项。
一、基本概念timeserial是一种基于时间戳的数据排序算法，它将一组数据按照时间顺序进行排序，以便于对数据进行管理和分析。
在timeserial 算法中，时间戳可以是数字、字符串或日期时间格式，它表示数据的时间信息。
timeserial算法可以处理不同类型的数据，如数字、字符串和文件等，并且可以处理大量数据。
二、使用方法要使用timeserial算法对一组数据进行排序，需要按照以下步骤进行操作：1. 准备数据：将需要排序的数据按照时间顺序进行组织，可以使用时间戳来表示时间信息。
2. 导入库：在程序中导入timeserial库，以便使用timeserial 算法对数据进行排序。
3. 调用排序函数：使用timeserial库中的sort_timeseries函数对数据进行排序。
该函数接受一组数据作为参数，并返回一个按时间顺序排列的数据集。
下面是一个使用timeserial算法对一组数据进行排序的示例代码：```pythonimport timeserial# 准备数据data = [("2023-07-01", "file1.txt"), ("2023-06-30", "file2.txt"), ("2023-07-15", "file3.txt")]# 对数据进行排序sorted_data = timeserial.sort_timeseries(data)# 输出排序结果for item in sorted_data:print(item)```在上面的示例中，我们首先准备了一组包含文件名和时间戳的数据，然后使用timeserial库中的sort_timeseries函数对数据进行排序，最后输出排序结果。
matlab中timeseries函数
matlab中timeseries函数MATLAB中的timeseries函数是一个非常有用的工具，它可以帮助我们处理时间序列数据。
时间序列数据是指按照时间顺序排列的数据，例如股票价格、气温、人口数量等。
在许多领域中，时间序列数据都是非常重要的，因为它们可以帮助我们预测未来的趋势和变化。
timeseries函数可以用来创建时间序列对象，这个对象可以包含时间序列数据和时间信息。
我们可以使用这个对象来进行各种操作，例如绘制时间序列图、计算时间序列的统计量、进行时间序列的滤波等等。
下面是一些常用的timeseries函数的用法：1. 创建时间序列对象我们可以使用timeseries函数来创建一个时间序列对象。
例如，下面的代码创建了一个包含10个随机数的时间序列对象：data = rand(10,1);time = datetime('now') + hours(1:10)';ts = timeseries(data,time);这个时间序列对象包含了10个随机数和对应的时间信息。
2. 绘制时间序列图我们可以使用plot函数来绘制时间序列图。
例如，下面的代码绘制了上面创建的时间序列对象的图像：plot(ts);这个图像显示了时间序列数据的变化情况。
3. 计算时间序列的统计量我们可以使用各种函数来计算时间序列的统计量。
例如，下面的代码计算了上面创建的时间序列对象的平均值和标准差：mean_ts = mean(ts);std_ts = std(ts);这些统计量可以帮助我们了解时间序列数据的分布情况。
4. 进行时间序列的滤波我们可以使用各种滤波器来对时间序列数据进行滤波。
例如，下面的代码使用了一个低通滤波器对时间序列数据进行滤波：[b,a] = butter(2,0.2);ts_filt = filtfilt(b,a,ts);这个代码使用了一个二阶巴特沃斯低通滤波器，将时间序列数据进行了平滑处理。
时间序列去趋势化处理
时间序列去趋势化处理英文回答：Time series data refers to a collection of data points that are recorded over a specific period of time. Analyzing time series data can provide valuable insights into patterns, trends, and seasonality in the data. However, in order to better understand the underlying patterns, it is often necessary to remove the trend component from the time series data. This process is known as detrending or trend removal.There are several methods available to detrend time series data. One common approach is to use a mathematical technique called linear regression. This involves fitting a straight line to the data and then subtracting the predicted values from the original data to obtain the detrended series. For example, let's say we have a time series of monthly sales data for a retail store. We can use linear regression to estimate the trend in the data andthen subtract the estimated trend from the original sales data to obtain the detrended series. This can help usidentify any deviations from the overall trend, such as seasonal spikes or unusual events.Another method for detrending time series data is the moving average technique. This involves calculating the average of a fixed number of data points and thensubtracting this average from each data point in the series. The moving average technique is particularly useful for smoothing out short-term fluctuations in the data and highlighting longer-term trends. For example, let's say we have a daily stock price series. By calculating a 30-day moving average and subtracting it from the original series, we can obtain a detrended series that shows the longer-term trend in the stock price.In addition to linear regression and moving average, there are other advanced techniques available fordetrending time series data. These include exponential smoothing, autoregressive integrated moving average (ARIMA) models, and Fourier analysis. Each technique has its ownadvantages and limitations, and the choice of method depends on the specific characteristics of the time series data and the research objectives.中文回答：时间序列数据是指在特定时间段内记录的一系列数据点。
数据挖掘_Ocean wave time series data(海浪时间序列数据集)
Ocean wave time series data(海浪时间序列数据集)数据摘要：These two time series were collected by Andy Jessup, Applied Physics Laboratory, University of Washington. Both time series record the height of ocean waves as a function of time, the first via a wire wave gauge and the second via an infrared wave gauge.中文关键词：数据挖掘,海浪,时间序列,单变量,二变量,英文关键词：Data mining,Ocean wave,Time series,Univariate,Bivariate,数据格式：TEXT数据用途：The data can be used for regression and analysis.数据详细介绍：Ocean wave time series data∙AbstractThese two time series were collected by Andy Jessup, Applied Physics Laboratory, University of Washington, and others and are described in the article "Breaking Waves Affecting Microwave Backscatter: 1.Detection and Verification" by A. T. Jessup, W. K. Melville and W. C.Keller, Journal of Geophysical Research, 96, C11, 20,547--59 (1991).∙Data DescriptionBoth time series record the height of ocean waves as a function of time, the first via a wire wave gauge and the second via an infrared wave gauge. Permission has been obtained to redistribute both time series.Questions concerning these series should be send to Don Percival (dbp@).It contians:WIRE WA VE GAUGE OCEAN W A VE TIME SERIESINFRARED W A VE GAUGE OCEAN W A VE TIME SERIESReferenceTime series used in "Spectral Analysis of Univariate and Bivariate Time Series" by D. B. Percival, Chapter 11 of "Statistical Methods for Physical Science," edited by J. L. Stanford and S. B. Vardeman, Academic Press, 1993数据预览：点此下载完整数据集。
时间序列数据、截面数据及面板数据的区别
时间序列数据、截⾯数据及⾯板数据的区别资料来源：百度百科
时间序列数据：time series data
截⾯数据：cross section data
⾯板数据：panel data
⾯板数据，即Panel Data，是截⾯数据与时间序列数据综合起来的⼀种数据类型。
其有时间序列和截⾯两个维度，当这类数据按两个维度排列时，是排在⼀个平⾯上，与只有⼀个维度的数据排在⼀条线上有着明显的不同，整个表格像是⼀个⾯板,所以把panel data译作“⾯板数据”。
但是,如果从其内在含义上讲,把panel data译
为“时间序列—截⾯数据” 更能揭⽰这类数据的本质上的特点。
也有译作“平⾏数据”或“TS-CS数据（Time Series - Cross Section）”。
1如
：城市名：北京、上海、重庆、天津的GDP分别为10、11、9、8（单位亿元）。
这就是截⾯数据，在⼀个时间点处切开，看各个城市的不同就是截⾯数据。
如：2000、2001、2002、2003、2004各年的北京市GDP分别为8、9、10、11、12（单位亿元）。
这就是时间序列，选⼀个城市，看各个样本时间点的不同就是时间序列。
2如
2000、2001、2002、2003、2004各年中国所有直辖市的GDP分别为：
北京市分别为8、9、10、11、12；
上海市分别为9、10、11、12、13；
天津市分别为5、6、7、8、9；
重庆市分别为7、8、9、10、11（单位亿元）。
这就是⾯板数据。
时序数据库InfluxDB的基本语法
时序数据库InfluxDB的基本语法⼀了解InfluxDB的必要性时序数据库主要存放的数据Time series data is a series of data points each associated with a specific time. Examples include:Server performance metricsFinancial averages over timeSensor data, such as temperature, barometric pressure, wind speeds, etc.时序数据库和关系数据库的区别Relational databases can be used to store and analyze time series data, but depending on the precision of your data, a query can involve potentially millions of rows. InfluxDB is purpose-built to store and query data by time, providing out-of-the-box functionality that optionally downsamples data after a specific age and a query engine optimized for time-based data.⼆基本概念2.1 database & durationdatabaseA logical container for users, retention policies, continuous queries, and time series data.durationThe attribute of the retention policy that determines how long InfluxDB stores data. Data older than the duration are automatically dropped from the database.2.2 fieldThe key-value pair in an InfluxDB data structure that records metadata and the actual data value. Fields are required in InfluxDB data structures and they are not indexed -queries on field values scan all points that match the specified time range and, as a result, are not performant relative to tags.Field keys are strings and they store metadata.Field values are the actual data; they can be strings, floats, integers, or booleans. A field value is always associated with a timestamp.2.3 TagsTags are optional. The key-value pair in the InfluxDB data structure that records metadata.You don’t need to have tags in your data structure, but it’s generally a good idea to make use of them because, unlike fields, tags are indexed. This means that queries on tags are faster and that tags are ideal for storing commonly-queried metadata.Tags 与 fields 的区别Tags are indexed and fields are not indexed. This means that queries on tags are more performant than those on fields.Tags 与 fields 的使⽤场景（1）Store commonly-queried meta data in tags（2）Store data in tags if you plan to use them with the InfluxQL GROUP BY clause（3）Store data in fields if you plan to use them with an InfluxQL function（4）Store numeric values as fields (tag values only support string values)2.4 measurementThe measurement acts as a container for tags, fields, and the time column, and the measurement name is the description of the data that are stored in the associated fields. Measurement names are strings, and, for any SQL users out there, a measurement is conceptually similar to a table.2.5 pointIn InfluxDB, a point represents a single data record, similar to a row in a SQL database table. Each point:has a measurement, a tag set, a field key, a field value, and a timestamp;is uniquely identified by its series and timestamp.You cannot store more than one point with the same timestamp in a series. If you write a point to a series with a timestamp that matches an existing point, the field set becomesa union of the old and new field set, and any ties go to the new field set.2.6 seriesIn InfluxDB, a series is a collection of points that share a measurement, tag set, and field key. A point represents a single data record that has four components: a measurement, tag set, field set, and a timestamp. A point is uniquely identified by its series and timestamp.series keyA series key identifies a particular series by measurement, tag set, and field key.三查询3.1 正则模糊查询1.实现查询以给定字段开始的数据select fieldName from measurementName where fieldName=~/^给定字段/2.实现查询以给定字段结束的数据select fieldName from measurementName where fieldName=~/给定字段$/3.实现查询包含给定字段数据select fieldName from measurementName where fieldName=~/给定字段/3.2 Select 注意事项:必须包含field keyA query requires at least one field key in the SELECT clause to return data. If the SELECT clause only includes a single tag key or several tag keys, the query returns an emptyresponse. This behavior is a result of how the system stores data.3.3 Where 限定使⽤单引号，否则⽆数据返回或报错（1）Single quote string field values in the WHERE clause. Queries with unquoted string field values or double quoted string field values will not return any data and, in mostcases,will not return an error.（2）Single quote tag values in the WHERE clause. Queries with unquoted tag values or double quoted tag values will not return any data and, in most cases, will not return anerror.3.4 Group By（1）Note that the GROUP BY clause must come after the WHERE clause.（2）The GROUP BY clause groups query results by: one or more specified tags ；specified time interval。
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• Long-Run Propensity β1 + β2 + β3 (the impact over time)
– Multicollinearity
(independent variables correlated each other)
Time Series Assumptions
1. 2. Linear in parameters (same as CLM) Zero Conditional Mean, E(ut|X) = 0, t=1,2, ....n If only true within years = contemporaneously exogenous (each period / eg. annual data) If true between years also = strictly exogenous No Perfect Collinearity (same as CLM) Homoskedasticity, Var(ut|X)=σ2 , t=1,2, ....n Over time No serial correlation (autocorrelation), Corr(ut,us|X)=0, for all t≠s Normality, ut ~ N(0, σ2) and independent of X
Lecture 13
Time Series: Basic Regression
Necessary reading
WOOLDRIDGE (2008): CHAPTER 10
Time
• Past may affect the future
• The impact on assumption 2
– Stochastic Process or Time Series Process
3. 4. 5. 6.
Functional Form
• To estimate the short-run elasticity and the long run elasticity estimate a log-log model and consider the impact propensity and the long-run propensity respectively • Dummy variables can be used to describe an event • Index Numbers (writing notes, according to the basic number)
• yt = β0 + β1t + β2t2 + et, t=1,2, ....
• Detrending – R squared
Hale Waihona Puke Seasonality• Monthly and Quarterly Data
– Seasonal Dummy Variables – Deseasonalizing
Trends
• Trends
– include a linear time trend
• yt = β0 + β1t + et, t=1,2, ....
– or an exponential trend
• log(yt) = β0 + β1t + et, t=1,2, ....
– or a quadratic time trend
• A sequence of random variables indexed by time
Static Models
• yt = β0 + β1 zt + ut
• Assumes that today’s y is explained by today’s z
Finite Distribution Lag Model
• Order two • yt = β0 + β1 zt + β2 zt-1 + β3 zt-2 + ut • Impact propensity β1 (the impact of today’s data) • Lag distribution (figure 10.1) (longer time period must be