Time Series Analysis and Forecasting using Python R - Formato Tapa dura
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Q760.00
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| Cuotas | Monto |
| 3 (Sin recargo) | Q253.33 |
| 6 (Sin recargo) | Q126.67 |
| 10 | Q80.56 |
| 12 | Q67.77 |
| 15 | Q54.47 |
| 18 | Q45.81 |
| 24 | Q35.63 |
| 36 | Q23.96 |
| 48 | Q18.13 |
| Cuotas | Monto |
| 3 (Sin recargo) | Q253.33 |
| 6 (Sin recargo) | Q126.67 |
| 10 (Sin recargo) | Q76.00 |
| 12 (Sin recargo) | Q63.33 |
| 15 | Q54.21 |
| 18 | Q45.18 |
| 24 | Q34.12 |
| 36 | Q23.49 |
| Cuotas | Monto |
| 3 | Q267.90 |
| 6 | Q135.53 |
| 10 | Q81.51 |
| 12 | Q68.40 |
| 18 | Q47.29 |
| 24 | Q35.63 |
| 36 | Q23.86 |
| Cuotas | Monto |
| 3 (Sin recargo) | Q253.33 |
| 6 (Sin recargo) | Q126.67 |
| 10 (Sin recargo) | Q76.00 |
| 12 (Sin recargo) | Q63.33 |
| 15 (Sin recargo) | Q50.67 |
| 18 (Sin recargo) | Q42.22 |
| 24 | Q33.88 |
| 36 | Q22.59 |
| 48 | Q16.94 |
| Cuotas | Monto |
| 2 (Sin recargo) | Q380.00 |
| 3 (Sin recargo) | Q253.33 |
| 6 | Q133.63 |
| 10 | Q80.56 |
| 12 | Q67.77 |
| 15 | Q54.97 |
| 18 | Q46.66 |
| Cuotas | Monto |
| 2 (Sin recargo) | Q380.00 |
| 3 (Sin recargo) | Q253.33 |
| 6 (Sin recargo) | Q126.67 |
| 10 (Sin recargo) | Q76.00 |
| 12 (Sin recargo) | Q63.33 |
| 15 | Q53.71 |
| 18 | Q45.18 |
| 24 | Q34.20 |
| 36 | Q23.01 |
| 48 | Q17.42 |
| Cuotas | Monto |
| 3 (Sin recargo) | Q253.33 |
| 6 (Sin recargo) | Q126.67 |
| 10 (Sin recargo) | Q76.00 |
| 12 (Sin recargo) | Q63.33 |
| 15 | Q53.71 |
| 18 | Q45.18 |
| 24 | Q34.83 |
| 36 | Q24.07 |
| 48 | Q17.73 |
| Cuotas | Monto |
| 3 (Sin recargo) | Q253.33 |
| 6 | Q133.63 |
| 10 | Q80.56 |
| 12 | Q67.77 |
| 15 | Q54.97 |
| 18 | Q46.66 |
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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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