Time series for data scientists: data management, description, modeling and forecasting
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2023
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xii, 463 Seiten Diagramme |
| ISBN: | 9781108837774 |
Internformat
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Datensatz im Suchindex
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| adam_text | Contents Preface Parti Descriptive Features of Time Series Data 1 Introduction to Time Series Data 1.1 Introduction 1.2 Where to Find Time Series Data? 1.3 Time Series Data Cleaning 1.4 Components of a Time Series 1.5 Hands-on Base (Native) R Code for Time Series 1.6 Time Series Data Visualization 1.7 Why Worry about Trends and Seasonality? 1.8 Feature Generation for ML Applications 1.9 Time Indexing 1.10 About R 1.11 Other Books on Time Series Using R 1.12 Problems 1.13 Quiz 1.14 Case Study: Using APIs to Access Time Series 2 Smoothing and Decomposing a Time Series 2.1 Introduction 2.2 Classical Decomposition 2.3 Classical Additive Decomposition 2.4 Classical Multiplicative Decomposition 2.5 Regression Smoothers 2.6 Exponential Smoothing 2.7 Prophet 2.8 Problems 2.9 Quiz 2.10 Case Study: LOWESS Smoothing with stl ( ) 3 Summary Statistics of Stationary Time Series 3.1 Introduction 3.2 The Mean and Variance of a Stationary Time Series page xi i 4 4 8 14 18 22 26 36 36 42 44 45 46 48 50 58 58 59 65 78 85 88 102 103 105 107 114 114 115
viii Contents 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 The Correlogram or Sample Autocorrelation Function (ACF) The Ljung-Box Test for White Noise The Partial Correlogram of a Time Series Time Series in the Frequency Domain The Typical ACF, PACF and Spectrum of a Nonstationary Time Series Applications in Unsupervised Machine Learning Problems Quiz Case Study: Multiple Seasonalities in Kaggle Competitions Appendix Part II Univariate Models of Temporal Dependence 4 The Algebra of Differencing and Backshifting 4.1 4.2 4.3 4.4 4.5 4.6 4.7 5 Stationary Stochastic Processes 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14 6 Introduction The Difference Operator and the Backshift Operator The Outcome of Differencing The Spectrum of Time Series after Differencing Problems Quiz Case Study: Smart Cities Introduction Stationary Stochastic Processes Theory Models for Stationary Stochastic Processes Autoregressive Stochastic Processes ARMA(p,q) Processes Practical Guidelines Transitioning to Statistical Inference Limitations of Stationary Stochastic Processes The Spectrum of Stochastic Processes Problems Quiz Case Study: COVID-19 Appendix A: Review of Introductory Probability Appendix B: A Time Series as a Vector Random Variable ARIMA(p, d, q)(P, D, Q)F Modeling and Forecasting 6.1 6.2 6.3 6.4 6.5 Introduction to ARIMAfp, d, q)(P, D, Q) Methodology The Practice of ARIMA Modeling and Forecasting Estimation More on Model Diagnostics Forecasting 116 128 131 135 139 143 144 147 149 156 159 162 162 163 173 174 175 177 180 189 189 190 202 216 225 226 227 229 230 230 234 236 241
243 245 245 249 273 275 278
Contents 6.6 6.7 6.8 6.9 6.10 6.11 6.12 Volatile Time Series Unit Root Tests Average (Consensus) Forecasts Problems Quiz Case Study: Automatic Forecasting at Scale Appendix: ARIMA(p, Ժ, ^)(P, D,Q)f Notations Partili Multivariate Modeling and Forecasting 7 Latent Process Models for Time Series 7.1 Summary Statistics for Binary and Categorical Data 7.2 7.3 7.4 7.5 7.6 7.7 7.8 8 Vector Autoregression 8.1 Introduction 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 8.11 8.12 9 Introduction Autocorrelated Time Series from Mixtures Autocorrelated Gaussian Mixtures Univariate State Space Models (SSM) Problems Quiz Appendix: First-Order Markov Process Cross-Correlation between Two Time Series Vector Autoregression Models (VAR) Applying VAR Models VAR Models for More than Two Time Series Automatic Fittings Impulse Response Functions (IRF) Spurious Relations, Stochastic Trends, Unit Roots and Cointegration VAR Models Software Problems Quiz Case Study Classical Regression with ARMA Residuals 9.1 Introduction 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 Causal Regression Analysis of Time Series Time Series Regression Regression Using the Box-Jenkins Methodology Intervention Models Problems Quiz Case Study: Forecasting at Scale Appendix: Review of Introductory ClassicalRegression ix 280 287 290 292 297 301 308 313 316 316 316 319 325 328 337 338 339 341 341 344 351 357 361 367 368 372 379 380 383 385 390 390 391 403 407 411 414 420 422 424
x Contents 10 Machine Learning Methods tor Time Series 10.1 10.2 10.3 10.4 10.5 10.6 Introduction ML-Based Regression DeepLearning Big Data Unsupervised Learning andFeature Generation Case Study: Smart Medical Devices References Index 431 431 433 439 444 444 446 448 458
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| adam_txt |
Contents Preface Parti Descriptive Features of Time Series Data 1 Introduction to Time Series Data 1.1 Introduction 1.2 Where to Find Time Series Data? 1.3 Time Series Data Cleaning 1.4 Components of a Time Series 1.5 Hands-on Base (Native) R Code for Time Series 1.6 Time Series Data Visualization 1.7 Why Worry about Trends and Seasonality? 1.8 Feature Generation for ML Applications 1.9 Time Indexing 1.10 About R 1.11 Other Books on Time Series Using R 1.12 Problems 1.13 Quiz 1.14 Case Study: Using APIs to Access Time Series 2 Smoothing and Decomposing a Time Series 2.1 Introduction 2.2 Classical Decomposition 2.3 Classical Additive Decomposition 2.4 Classical Multiplicative Decomposition 2.5 Regression Smoothers 2.6 Exponential Smoothing 2.7 Prophet 2.8 Problems 2.9 Quiz 2.10 Case Study: LOWESS Smoothing with stl ( ) 3 Summary Statistics of Stationary Time Series 3.1 Introduction 3.2 The Mean and Variance of a Stationary Time Series page xi i 4 4 8 14 18 22 26 36 36 42 44 45 46 48 50 58 58 59 65 78 85 88 102 103 105 107 114 114 115
viii Contents 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 The Correlogram or Sample Autocorrelation Function (ACF) The Ljung-Box Test for White Noise The Partial Correlogram of a Time Series Time Series in the Frequency Domain The Typical ACF, PACF and Spectrum of a Nonstationary Time Series Applications in Unsupervised Machine Learning Problems Quiz Case Study: Multiple Seasonalities in Kaggle Competitions Appendix Part II Univariate Models of Temporal Dependence 4 The Algebra of Differencing and Backshifting 4.1 4.2 4.3 4.4 4.5 4.6 4.7 5 Stationary Stochastic Processes 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14 6 Introduction The Difference Operator and the Backshift Operator The Outcome of Differencing The Spectrum of Time Series after Differencing Problems Quiz Case Study: Smart Cities Introduction Stationary Stochastic Processes Theory Models for Stationary Stochastic Processes Autoregressive Stochastic Processes ARMA(p,q) Processes Practical Guidelines Transitioning to Statistical Inference Limitations of Stationary Stochastic Processes The Spectrum of Stochastic Processes Problems Quiz Case Study: COVID-19 Appendix A: Review of Introductory Probability Appendix B: A Time Series as a Vector Random Variable ARIMA(p, d, q)(P, D, Q)F Modeling and Forecasting 6.1 6.2 6.3 6.4 6.5 Introduction to ARIMAfp, d, q)(P, D, Q) Methodology The Practice of ARIMA Modeling and Forecasting Estimation More on Model Diagnostics Forecasting 116 128 131 135 139 143 144 147 149 156 159 162 162 163 173 174 175 177 180 189 189 190 202 216 225 226 227 229 230 230 234 236 241
243 245 245 249 273 275 278
Contents 6.6 6.7 6.8 6.9 6.10 6.11 6.12 Volatile Time Series Unit Root Tests Average (Consensus) Forecasts Problems Quiz Case Study: Automatic Forecasting at Scale Appendix: ARIMA(p, Ժ, ^)(P, D,Q)f Notations Partili Multivariate Modeling and Forecasting 7 Latent Process Models for Time Series 7.1 Summary Statistics for Binary and Categorical Data 7.2 7.3 7.4 7.5 7.6 7.7 7.8 8 Vector Autoregression 8.1 Introduction 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 8.11 8.12 9 Introduction Autocorrelated Time Series from Mixtures Autocorrelated Gaussian Mixtures Univariate State Space Models (SSM) Problems Quiz Appendix: First-Order Markov Process Cross-Correlation between Two Time Series Vector Autoregression Models (VAR) Applying VAR Models VAR Models for More than Two Time Series Automatic Fittings Impulse Response Functions (IRF) Spurious Relations, Stochastic Trends, Unit Roots and Cointegration VAR Models Software Problems Quiz Case Study Classical Regression with ARMA Residuals 9.1 Introduction 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 Causal Regression Analysis of Time Series Time Series Regression Regression Using the Box-Jenkins Methodology Intervention Models Problems Quiz Case Study: Forecasting at Scale Appendix: Review of Introductory ClassicalRegression ix 280 287 290 292 297 301 308 313 316 316 316 319 325 328 337 338 339 341 341 344 351 357 361 367 368 372 379 380 383 385 390 390 391 403 407 411 414 420 422 424
x Contents 10 Machine Learning Methods tor Time Series 10.1 10.2 10.3 10.4 10.5 10.6 Introduction ML-Based Regression DeepLearning Big Data Unsupervised Learning andFeature Generation Case Study: Smart Medical Devices References Index 431 431 433 439 444 444 446 448 458 |
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| title | Time series for data scientists data management, description, modeling and forecasting |
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| title_full | Time series for data scientists data management, description, modeling and forecasting Juana Sanchez |
| title_fullStr | Time series for data scientists data management, description, modeling and forecasting Juana Sanchez |
| title_full_unstemmed | Time series for data scientists data management, description, modeling and forecasting Juana Sanchez |
| title_short | Time series for data scientists |
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| title_sub | data management, description, modeling and forecasting |
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