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首页Practical Time Series Forecasting with R A Hands-On Guide, 2nd Shmueli 2016

Practical Time Series Forecasting with R A Hands-On Guide, 2nd S...

Time

Series

Forecasting

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Practical Time Series Forecasting with R A Hands-On Guide, 2nd Edition Shmueli 2016

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G A L I T S H M U E L I

K E N N E T H C . L I C H T E N D A H L J R .

P R A C T I C A L

T I M E S E R I E S

F O R E C A S T I N G

W I T H R

A H A N D S - O N G U I D E

S E C O N D E D I T I O N

A X E L R O D S C H N A L L P U B L I S H E R S

published by axelrod schnall publishers

isbn-13: 978-0-9978479-1-8

isbn-10: 0-9978479-1-3

stored or used in any form or by any means graphic, electronic, or mechanical, including but

not limited to photocopying, recording, scanning, digitizing, taping, Web distribution, infor-

mation networks or information storage and retrieval systems, or in any manner whatsoever

without prior written permission.

For further information see www.forecastingbook.com

Second Edition, July 2016

Contents

Preface 9

1 Approaching Forecasting 15

1.1 Forecasting: Where? . . . . . . . . . . . . . . . . . . 15

1.2 Basic Notation . . . . . . . . . . . . . . . . . . . . . . 15

1.3 The Forecasting Process . . . . . . . . . . . . . . . . 16

1.4 Goal Deﬁnition . . . . . . . . . . . . . . . . . . . . . 18

1.5 Problems . . . . . . . . . . . . . . . . . . . . . . . . . 23

2 Time Series Data 25

2.1 Data Collection . . . . . . . . . . . . . . . . . . . . . 25

2.2 Time Series Components . . . . . . . . . . . . . . . . 28

2.3 Visualizing Time Series . . . . . . . . . . . . . . . . . 30

2.4 Interactive Visualization . . . . . . . . . . . . . . . . 35

2.5 Data Pre-Processing . . . . . . . . . . . . . . . . . . . 39

2.6 Problems . . . . . . . . . . . . . . . . . . . . . . . . . 42

3 Performance Evaluation 45

3.1 Data Partitioning . . . . . . . . . . . . . . . . . . . . 45

3.2 Naive Forecasts . . . . . . . . . . . . . . . . . . . . . 50

3.3 Measuring Predictive Accuracy . . . . . . . . . . . . 51

3.4 Evaluating Forecast Uncertainty . . . . . . . . . . . 55

3.5 Advanced Data Partitioning: Roll-Forward Validation 62

3.6 Example: Comparing Two Models . . . . . . . . . . 65

3.7 Problems . . . . . . . . . . . . . . . . . . . . . . . . . 67

4 Forecasting Methods: Overview 69

4.1 Model-Based vs. Data-Driven Methods . . . . . . . 69

4.2 Extrapolation Methods, Econometric Models, and Ex-

ternal Information . . . . . . . . . . . . . . . . . . . 70

4.3 Manual vs. Automated Forecasting . . . . . . . . . 72

4.4 Combining Methods and Ensembles . . . . . . . . . 73

4.5 Problems . . . . . . . . . . . . . . . . . . . . . . . . . 77

5 Smoothing Methods 79

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 79

5.2 Moving Average . . . . . . . . . . . . . . . . . . . . . 80

5.3 Differencing . . . . . . . . . . . . . . . . . . . . . . . 85

5.4 Simple Exponential Smoothing . . . . . . . . . . . . 87

5.5 Advanced Exponential Smoothing . . . . . . . . . . 90

5.6 Summary of Exponential Smoothing in R Using ets 98

5.7 Extensions of Exponential Smoothing . . . . . . . . 101

5.8 Problems . . . . . . . . . . . . . . . . . . . . . . . . . 107

6 Regression Models: Trend & Seasonality 117

6.1 Model with Trend . . . . . . . . . . . . . . . . . . . . 117

6.2 Model with Seasonality . . . . . . . . . . . . . . . . 125

6.3 Model with Trend and Seasonality . . . . . . . . . . 129

6.4 Creating Forecasts from the Chosen Model . . . . . 132

6.5 Problems . . . . . . . . . . . . . . . . . . . . . . . . . 133

7 Regression Models: Autocorrelation & External Info 143

7.1 Autocorrelation . . . . . . . . . . . . . . . . . . . . . 143

7.2 Improving Forecasts by Capturing Autocorrelation:

AR and ARIMA Models . . . . . . . . . . . . . . . . 147

7.3 Evaluating Predictability . . . . . . . . . . . . . . . . 153

7.4 Including External Information . . . . . . . . . . . . 154

7.5 Problems . . . . . . . . . . . . . . . . . . . . . . . . . 170

8 Forecasting Binary Outcomes 179

8.1 Forecasting Binary Outcomes . . . . . . . . . . . . . 179

8.2 Naive Forecasts and Performance Evaluation . . . . 180

8.3 Logistic Regression . . . . . . . . . . . . . . . . . . . 181

8.4 Example: Rainfall in Melbourne, Australia . . . . . 183

8.5 Problems . . . . . . . . . . . . . . . . . . . . . . . . . 187

9 Neural Networks 189

9.1 Neural Networks for Forecasting Time Series . . . . 189

9.2 The Neural Network Model . . . . . . . . . . . . . . 190

9.3 Pre-Processing . . . . . . . . . . . . . . . . . . . . . . 194

9.4 User Input . . . . . . . . . . . . . . . . . . . . . . . . 195

9.5 Forecasting with Neural Nets in R . . . . . . . . . . 196

9.6 Example: Forecasting Amtrak Ridership . . . . . . . 198

9.7 Problems . . . . . . . . . . . . . . . . . . . . . . . . . 201

10 Communication and Maintenance 203

10.1 Presenting Forecasts . . . . . . . . . . . . . . . . . . 203

10.2 Monitoring Forecasts . . . . . . . . . . . . . . . . . . 205

10.3 Written Reports . . . . . . . . . . . . . . . . . . . . . 206

10.4 Keeping Records of Forecasts . . . . . . . . . . . . . 207

10.5 Addressing Managerial "Forecast Adjustment" . . . 208

11 Cases 211

11.1 Forecasting Public Transportation Demand . . . . . 211

11.2 Forecasting Tourism (2010 Competition, Part I) . . . 215

11.3 Forecasting Stock Price Movements (2010 INFORMS

Competition) . . . . . . . . . . . . . . . . . . . . . . . 219

Data Resources, Competitions, and Coding Resources 225

Bibliography 227

Index 231

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