Forecasting: principles and practice
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Lexington, Ky.
Otexts
May 2018
|
| Ausgabe: | Second print edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Auf dem Umschlag: A comprehensive introduction to the latest forecasting methods using R ; learn to improve your forecast accuracy using dozens of real data examples. Literaturverzeichnis: Seite 373-377 |
| Beschreibung: | 382 Seiten Diagramme |
| ISBN: | 9780987507112 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804178618880884736 |
|---|---|
| adam_text | Contents
Preface 7
1 Getting started 11
1 1 What can be forecast? 12
1 2 Forecasting, planning and goals 14
1 3 Determining what to forecast 15
1 4 Forecasting data and methods 16
1 5 Some case studies 19
1 6 The basic steps in a forecasting task 21
1 7 The statistical forecasting perspective 23
1 8 Exercises 25
1 9 Further reading 25
2 Time series graphics 27
2 1 ts objects 27
2 2 Time plots 29
2 3 Time series patterns 31
2 4 Seasonal plots 32
2 5 Seasonal subseries plots 34
2 6 Scatterplots 35
2 7 Lag plots 39
2 8 Autocorrelation 40
2 9 White noise 42
2 10 Exercises 43
2 11 Further reading 46
3 The forecaster s toolbox 47
3 1 Some simple forecasting methods 47
3 2 Transformations and adjustments 50
3 3 Residual diagnostics 56
4 forecasting: principles and practice
3 4 Evaluating forecast accuracy 62
3 5 Prediction intervals 71
3 6 The forecast package in R 76
3 7 Exercises 78
3 8 Further reading 81
4 Judgmental forecasts 83
4 1 Beware of limitations 84
4 2 Key principles 85
4 3 The Delphi method 90
4 4 Forecasting by analogy 93
4 5 Scenario forecasting 95
4 6 New product forecasting 96
4 7 Judgmental adjustments 98
4 8 Further reading 101
5 Time series regression models 103
5 1 The linear model 103
5 2 Least squares estimation 109
5 3 Evaluating the regression model 114
5 4 Some useful predictors 121
5 5 Selecting predictors 129
5 6 Forecasting with regression 135
5 7 Matrix formulation 141
5 8 Nonlinear regression 143
5 9 Correlation, causation and forecasting 149
5 10 Exercises 152
5 11 Further reading 156
6 Time series decomposition 157
6 1 Time series components 157
6 2 Moving averages 161
6 3 Classical decomposition 167
6 4 XI1 decomposition 170
6 5 SEATS decomposition 173
6 6 STL decomposition 174
6 7 Measuring strength of trend and seasonality 176
6 8 Forecasting with decomposition 177
6 9 Exercises 180
6 10 Further reading 183
CONTENTS 5
7 Exponential smoothing 185
7 1 Simple exponential smoothing 186
7 2 Trend methods 193
7 3 Holt-Winters seasonal method 198
74A taxonomy of exponential smoothing methods 204
7 5 Innovations state space models for exponential smoothing 205
7 6 Estimation and model selection 209
7 7 Forecasting with ETS models 215
7 8 Exercises 218
7 9 Further reading 222
8 ARIMA models 223
8 1 Stationarity and differencing 223
8 2 Backshift notation 232
8 3 Autoregressive models 233
8 4 Moving average models 234
8 5 Non-seasonal ARIMA models 236
8 6 Estimation and order selection 243
8 7 ARIMA modelling in R 244
8 8 Forecasting 252
8 9 Seasonal ARIMA models 254
8 10 ARIMA vs ETS 264
8 11 Exercises 270
8 12 Further reading 276
9 Dynamic regression models 277
9 1 Estimation 278
9 2 Regression with ARIMA errors in R 279
9 3 Forecasting 282
9 4 Stochastic and deterministic trends 286
9 5 Dynamic harmonic regression 289
9 6 Lagged predictors 292
9 7 Exercises 295
9 8 Further reading 298
10 Forecasting hierarchical or grouped time series 299
10 1 Hierarchical time series 300
10 2 Grouped time series 305
10 3 The bottom-up approach 309
10 4 Top-down approaches 310
10 5 Middle-out approach 314
6 forecasting: principles and practice
10 6 Mapping matrices 314
10 7 The optimal reconciliation approach 316
10 8 Exercises 322
10 9 Further reading 322
11 Advanced forecasting methods 323
11 1 Complex seasonality 323
11 2 Vector autoregressions 331
11 3 Neural network models 335
11 4 Bootstrapping and bagging 341
11 5 Exercises 346
11 6 Further reading 347
12 Some practical forecasting issues 349
12 1 Weekly, daily and sub-daily data 349
12 2 Time series of counts 352
12 3 Ensuring forecasts stay within limits 354
12 4 Forecast combinations 357
12 5 Prediction intervals for aggregates 358
12 6 Backcasting 359
12 7 Very long and very short time series 361
12 8 Forecasting on training and test sets 363
12 9 Dealing with missing values and outliers 365
12 10 Further reading 369
Appendix: Using R 371
Bibliography 373
Index
|
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| discipline | Mathematik Wirtschaftswissenschaften |
| edition | Second print edition |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:06:50Z |
| institution | BVB |
| isbn | 9780987507112 |
| language | English |
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| spelling | Hyndman, Rob J. Verfasser (DE-588)135530091 aut Forecasting principles and practice Rob J. Hyndman and George Athanasopoulos Second print edition Lexington, Ky. Otexts May 2018 382 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Auf dem Umschlag: A comprehensive introduction to the latest forecasting methods using R ; learn to improve your forecast accuracy using dozens of real data examples. Literaturverzeichnis: Seite 373-377 Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Unternehmensplanung (DE-588)4078609-2 gnd rswk-swf Unternehmensplanung (DE-588)4078609-2 s Prognoseverfahren (DE-588)4358095-6 s b DE-604 Athanasopoulos, George Verfasser (DE-588)131525719 aut HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030400257&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hyndman, Rob J. Athanasopoulos, George Forecasting principles and practice Prognoseverfahren (DE-588)4358095-6 gnd Unternehmensplanung (DE-588)4078609-2 gnd |
| subject_GND | (DE-588)4358095-6 (DE-588)4078609-2 |
| title | Forecasting principles and practice |
| title_auth | Forecasting principles and practice |
| title_exact_search | Forecasting principles and practice |
| title_full | Forecasting principles and practice Rob J. Hyndman and George Athanasopoulos |
| title_fullStr | Forecasting principles and practice Rob J. Hyndman and George Athanasopoulos |
| title_full_unstemmed | Forecasting principles and practice Rob J. Hyndman and George Athanasopoulos |
| title_short | Forecasting |
| title_sort | forecasting principles and practice |
| title_sub | principles and practice |
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| topic_facet | Prognoseverfahren Unternehmensplanung |
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