{"product_id":"time-series-analysis-and-forecasting-using-python-r-9781716451133","title":"Time Series Analysis and Forecasting using Python \u0026 R","description":"\u003cp\u003e • Author(s): Jeffrey Strickland\u003cbr\u003e • Publisher: Lulu.com\u003cbr\u003e • Publisher Imprint: Lulu.com\u003cbr\u003e • BISAC: Languages - Python\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e","brand":"Atlantic Books","offers":[{"title":"Hardcover","offer_id":46453170274455,"sku":"9781716451133","price":6956.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781716451133.webp?v=1769143624","url":"https:\/\/atlanticbooks.com\/products\/time-series-analysis-and-forecasting-using-python-r-9781716451133","provider":"Atlantic Books","version":"1.0","type":"link"}