Average and exponential smoothing models

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Statistics, Data Analysis, and Decision Modeling

 

FOURTH EDITION

James R. Evans

 

 

9780558689766

 

Chapter 7 Forecasting

 

Introduction

 

 

 

QUALITATIVE AND JUDGMENTAL METHODS

 

Historical Analogy

 

The Delphi Method

 

Indicators and Indexes for Forecasting

 

 

 

STATISTICAL FORECASTING MODELS

 

 

 

FORECASTING MODELS FOR STATIONARY TIME SERIES

 

Moving Average Models

 

Error Metrics and Forecast Accuracy

 

Exponential Smoothing Models

 

 

 

FORECASTING MODELS FOR TIME SERIES WITH TREND AND SEASONALITY

 

Models for Linear Trends

 

Models for Seasonality

 

Models for Trend and Seasonality

 

 

 

CHOOSING AND OPTIMIZING FORECASTING MODELS USING CB PREDICTOR

 

 

 

REGRESSION MODELS FOR FORECASTING

 

Autoregressive Forecasting Models

 

Incorporating Seasonality in Regression Models

 

Regression Forecasting with Causal Variables

 

 

 

THE PRACTICE OF FORECASTING

 

 

 

BASIC CONCEPTS REVIEW QUESTIONS

 

 

 

SKILL-BUILDING EXERCISES

 

SKILL-BUILDING EXERCISES

 

 

 

PROBLEMS AND APPLICATIONS

 

 

 

CASE: ENERGY FORECASTING

 

 

 

APPENDIX: ADVANCED FORECASTING MODELS—THEORY AND COMPUTATION

 

Double Moving Average

 

Double Exponential Smoothing

 

Additive Seasonality

 

Multiplicative Seasonality

 

Holt–Winters Additive Model

 

Holt– –Winters Multiplicative Model

 

INTRODUCTION

 

 

 

One of the major problems that managers face is forecasting future events in order to make good decisions. For example, forecasts of interest rates, energy prices, and other economic indicators are needed for financial planning; sales forecasts are needed to plan production and workforce capacity; and forecasts of trends in demographics, consumer behavior, and technological innovation are needed for long-term strategic planning. The government also invests significant resources on predicting short-run U.S. business performance using the Index of Leading Indicators. This index focuses on the performance of individual businesses, which often is highly correlated with the performance of the overall economy, and is used to forecast economic trends for the nation as a whole. In this chapter, we introduce some common methods and approaches to forecasting, including both qualitative and quantitative techniques.

 

Managers may choose from a wide range of forecasting techniques. Selecting the appropriate method depends on the characteristics of the forecasting problem, such as the time horizon of the variable being forecast, as well as available information on which the forecast will be based. Three major categories of forecasting approaches are qualitative and judgmental techniques, statistical time-series models, and explanatory/causal methods.

 

 

 

Qualitative and judgmental techniques rely on experience and intuition; they are necessary when historical data are not available or when the decision maker needs to forecast far into the future. For example, a forecast of when the next generation of a microprocessor will be available and what capabilities it might have will depend greatly on the opinions and expertise of individuals who understand the technology.

 

 

 

Statistical time-series models find greater applicability for short-range forecasting problems. A time series is a stream of historical data, such as weekly sales. Time-series models assume that whatever forces have influenced sales in the recent past will continue into the near future; thus, forecasts are developed by extrapolating these data into the future.

 

Explanatory/causal models seek to identify factors that explain statistically the patterns observed in the variable being forecast, usually with regression analysis. While time-series models use only time as the independent variable, explanatory/causal models generally include other factors. For example, forecasting the price of oil might incorporate independent variables such as the demand for oil (measured in barrels), the proportion of oil stock generated by OPEC countries, and tax rates. Although we can never prove that changes in these variables actually cause changes in the price of oil, we often have evidence that a strong influence exists.

 

Surveys of forecasting practices have shown that both judgmental and quantitative methods are used for forecasting sales of product lines or product families, as well as for broad company and industry forecasts. Simple time-series models are used for short- and medium-range forecasts, whereas regression analysis is the most popular method for long-range forecasting. However, many companies rely on judgmental methods far more than quantitative methods, and almost half judgmentally adjust quantitative forecasts.

 

In this chapter, we focus on these three approaches to forecasting. Specifically, we will discuss the following:

 

Historical analogy and the Delphi method as approaches to judgmental forecasting

 

Moving average and exponential smoothing models for time-series forecasting, with a discussion of evaluating the quality of forecasts

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