Forecasting

Forecasting

Why forecast?

Features Common to all Forecasts

∙Conditions in the past will continue in the future

∙Rarely perfect

∙Forecasts for groups tend to be more accurate than forecasts for individuals ∙Forecast accuracy declines as time horizon increases

Elements of a Good Forecast

∙Timely

∙Accurate

∙Reliable (should work consistently)

∙Forecast expressed in meaningful units

∙Communicated in writing

∙Simple to understand and use

Steps in Forecasting Process

∙Determine purpose of the forecast

∙Establish a time horizon

∙Select forecasting technique

∙Gather and analyze the appropriate data

∙Prepare the forecast

∙Monitor the forecast

Types of Forecasts

∙Qualitative

o Judgment and opinion

o Sales force

o Consumer surveys

o Delphi technique

∙Quantitative

o Regression and Correlation (associative)

o Time series

Forecasts Based on Time Series Data

∙What is Time Series?

∙Components (behavior) of Time Series data

o Trend

o Cycle

o Seasonal

o Irregular

o Random variations

Naïve Methods

Naïve Forecast – uses a single previous value of a time series as the basis of a forecast.

Techniques for Averaging

∙What is the purpose of averaging?

∙Common Averaging Techniques

o Moving Averages

o Exponential smoothing

Moving Average

Exponential Smoothing

Techniques for Trend

Linear Trend Equation

line the of slope at of value pe riod time for fore cast from pe riods time of numbe r spe cifie d =====b t

y a t

y t t where t t 0:

Curvilinear Trend Equation

line the of slope at of value pe riod time for fore cast from pe riods time of numbe r spe cifie d =====b t

y a t

y t t where t t 0:

Techniques for Seasonality

∙ What is seasonality?

∙ What are seasonal relatives or indexes?

∙ How seasonal indexes are used:

o Deseasonalizing data

o Seasonalizing data

∙ How indexes are computed (see Example 7 on page 109)

Accuracy and Control of Forecasts

Measures of Accuracy

o Mean Absolute Deviation (MAD)

o Mean Squared Error (MSE)

o Mean Absolute Percentage Error (MAPE) Forecast Control Measure

o Tracking Signal

Mean Absolute Deviation (MAD)

Mean Squared Error (or Deviation) (MSE)

Mean Square Percentage Error (MAPE)

Tracking Signal

Problems:

2 – Plot, Linear, MA, exponential Smoothing

5 – Applying a linear trend to forecast

15 – Computing seasonal relatives

17 – Using indexes to deseasonalize values

26 – Using MAD, MSE to measure forecast accuracy