Description: 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-19 Chapter 4: Simple exponential smoothing (SES), Holt's and Holt-Winter's double and triple exponential smoothing Chapter 5: Python programming in Jupyter Notebook for the concepts covered in Chapters 2, 3 and 4 Chapter 6: Stationarity and differencing, including unit root tests. Chapter 7: ARIMA and SARMIA (seasonal) modeling and forecast development Chapter 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.
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End Time: 2025-02-07T20:53:10.000Z
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Book Title: Time Series Analysis and Forecasting using Python & R
Topic: exponential smoothing, Holt-Winters, stationarity, seasonality, t
Genre: Business & Economics
Original Language: English
Number of Pages: 448 Pages
Language: English
Publication Name: Time Series Analysis and Forecasting Using Python & R
Publisher: Lulu Press, Inc.
Item Height: 1 in
Subject: Programming Languages / Python
Publication Year: 2020
Type: Textbook
Item Weight: 27.1 Oz
Item Length: 9 in
Author: Jeffrey Strickland
Subject Area: Computers
Item Width: 6 in
Format: Hardcover
