Time Series Analysis and Forecasting using Python R
Precio:
Q865.00
Actualmente no disponible
- 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 "Wheres 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), Holts and Holt-Winters 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.
* Precio y unidades sujetos a revisión de Pacifiko
Información del producto
| PID | ZDEzN2YzMz |
| Número de modelo | 1716451132 |
| Garantía | 7 días sujeto a las políticas de Tienda Mundial |
Garantía y Soporte
Para más información sobre Garantías en Pacifiko visitar la siguiente pagina: Garantías