Time Series Analysis and Forecasting using Python R
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Q865.00
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| Cuotas | Monto |
| 3 (Sin recargo) | Q288.33 |
| 6 (Sin recargo) | Q144.17 |
| 10 | Q91.69 |
| 12 | Q77.13 |
| 15 | Q61.99 |
| 18 | Q52.14 |
| 24 | Q40.55 |
| 36 | Q27.27 |
| 48 | Q20.63 |
| Cuotas | Monto |
| 3 (Sin recargo) | Q288.33 |
| 6 (Sin recargo) | Q144.17 |
| 10 (Sin recargo) | Q86.50 |
| 12 (Sin recargo) | Q72.08 |
| 15 | Q61.70 |
| 18 | Q51.42 |
| 24 | Q38.83 |
| 36 | Q26.73 |
| Cuotas | Monto |
| 3 | Q304.91 |
| 6 | Q154.26 |
| 10 | Q92.77 |
| 12 | Q77.85 |
| 18 | Q53.82 |
| 24 | Q40.55 |
| 36 | Q27.15 |
| Cuotas | Monto |
| 3 (Sin recargo) | Q288.33 |
| 6 (Sin recargo) | Q144.17 |
| 10 (Sin recargo) | Q86.50 |
| 12 (Sin recargo) | Q72.08 |
| 15 (Sin recargo) | Q57.67 |
| 18 (Sin recargo) | Q48.06 |
| 24 | Q38.56 |
| 36 | Q25.71 |
| 48 | Q19.28 |
| Cuotas | Monto |
| 2 (Sin recargo) | Q432.50 |
| 3 (Sin recargo) | Q288.33 |
| 6 | Q152.10 |
| 10 | Q91.69 |
| 12 | Q77.13 |
| 15 | Q62.57 |
| 18 | Q53.10 |
| Cuotas | Monto |
| 2 (Sin recargo) | Q432.50 |
| 3 (Sin recargo) | Q288.33 |
| 6 | Q152.10 |
| 10 | Q91.69 |
| 12 | Q77.13 |
| 15 | Q62.57 |
| 18 | Q53.10 |
| 24 | Q40.55 |
| 36 | Q27.15 |
| 48 | Q20.45 |
| Cuotas | Monto |
| 3 (Sin recargo) | Q288.33 |
| 6 (Sin recargo) | Q144.17 |
| 10 (Sin recargo) | Q86.50 |
| 12 (Sin recargo) | Q72.08 |
| 15 | Q61.13 |
| 18 | Q51.42 |
| 24 | Q39.65 |
| 36 | Q27.39 |
| 48 | Q20.18 |
| Cuotas | Monto |
| 3 (Sin recargo) | Q288.33 |
| 6 | Q152.10 |
| 10 | Q91.69 |
| 12 | Q77.13 |
| 15 | Q62.57 |
| 18 | Q53.10 |
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- 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.
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Información del producto
| PID | ZDEzN2YzMz |
| Número de modelo | 1716451132 |
| Garantía | 7 días sujeto a las políticas de Tienda Mundial |
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