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Time Series Analysis and Forecasting using Python & R
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(Buch) |
Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!
Lieferstatus: |
i.d.R. innert 14-24 Tagen versandfertig |
Veröffentlichung: |
November 2020
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Genre: |
EDV / Informatik |
ISBN: |
9781716451133 |
EAN-Code:
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9781716451133 |
Verlag: |
Lulu.com |
Einband: |
Gebunden |
Sprache: |
English
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Dimensionen: |
H 235 mm / B 157 mm / D 29 mm |
Gewicht: |
797 gr |
Seiten: |
448 |
Zus. Info: |
HC gerader Rücken kaschiert |
Bewertung: |
Titel bewerten / Meinung schreiben
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Inhalt: |
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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