MOMENTAN AUSVERKAUFT
Über dieses Produkt
Product Identifiers
PublisherLulu Press, Inc.
ISBN-101716451132
ISBN-139781716451133
eBay Product ID (ePID)9050389344
Product Key Features
Number of Pages448 Pages
LanguageEnglish
Publication NameTime Series Analysis and Forecasting Using Python & R
SubjectProgramming Languages / Python
Publication Year2020
TypeTextbook
AuthorJeffrey Strickland
Subject AreaComputers
FormatHardcover
Dimensions
Item Height1 in
Item Weight27.1 Oz
Item Length9 in
Item Width6 in
Additional Product Features
Intended AudienceTrade
SynopsisThis 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.
