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TIME SERIES ANALYSIS AND FORECASTING USING PYTHON & R IBD

LULU.COM
11 / 2020
9781716451133
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Sinopsis

This book full-color textbook assumes a basic understanding of statistics and mathematical or statistical modeling. Although a little programming experience would be nice, but it is not required. We use current real-world data, like COVID-19, to motivate times series analysis have three thread problems that appear in nearly every chapter: 'Got Milk?', 'Got a Job?' and 'Where?s the Beef?' Chapter 1: Loading data in the R-Studio and Jupyter Notebook environments. Chapter 2: Components of a times series and decomposition Chapter 3: Moving averages (MAs) and COVID-19Chapter 4: Simple exponential smoothing (SES), Holt?s and Holt-Winter?s double and triple exponential smoothingChapter 5: Python programming in Jupyter Notebook for the concepts covered in Chapters 2, 3 and 4Chapter 6: Stationarity and differencing, including unit root tests. Chapter 7: ARIMA and SARMIA (seasonal) modeling and forecast developmentChapter 8: ARIMA modeling using Python Chapter 9: Structural models and analysis using unobserved component models (UCMs)Chapter 10: Advanced time series analysis, including time-series interventions, exogenous regressors, and vector autoregressive (VAR) processes.

PVP
90,48