Python for Time Series Forecasting 2025

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Files
  1. Get Bonus Downloads Here.url180 bytes
  2. 1. Why learn practical Python for time series forecasting.mp43.8 MB
  3. 1. Why learn practical Python for time series forecasting.srt1.0 KB
  4. 2. How to use Codespaces.mp49.2 MB
  5. 2. How to use Codespaces.srt4.6 KB
  6. 1. Search and download Federal Reserve Economic Data.mp44.5 MB
  7. 1. Search and download Federal Reserve Economic Data.srt1.9 KB
  8. 2. Load CSV and set dtype as datetime.mp412.6 MB
  9. 2. Load CSV and set dtype as datetime.srt6.8 KB
  10. 3. Datetime components on different columns.mp42.4 MB
  11. 3. Datetime components on different columns.srt1.4 KB
  12. 4. Why set the datetime column as index.mp48.4 MB
  13. 4. Why set the datetime column as index.srt4.9 KB
  14. 5. Load and preprocess data from Excel.mp45.6 MB
  15. 5. Load and preprocess data from Excel.srt3.4 KB
  16. 1. Configure a template notebook based on new datasets.mp439.8 MB
  17. 1. Configure a template notebook based on new datasets.srt16.6 KB
  18. 1. SARIMA vs. exponential smoothing.mp43.5 MB
  19. 1. SARIMA vs. exponential smoothing.srt1.9 KB
  20. 2. Model fit and forecast.mp47.2 MB
  21. 2. Model fit and forecast.srt3.0 KB
  22. 3. Understand model configurations based on playground.mp48.4 MB
  23. 3. Understand model configurations based on playground.srt3.8 KB
  24. 4. Diagnostics to validate assumptions and inform model choice.mp47.7 MB
  25. 4. Diagnostics to validate assumptions and inform model choice.srt3.6 KB
  26. 1. Introduction to Prophet A semi-automatic time series model.mp46.7 MB
  27. 1. Introduction to Prophet A semi-automatic time series model.srt2.8 KB
  28. 2. Model fit step by step.mp416.8 MB
  29. 2. Model fit step by step.srt7.3 KB
  30. 3. Feed holidays data into the model.mp45.8 MB
  31. 3. Feed holidays data into the model.srt2.4 KB
  32. 4. Data preprocessing to forecast and visualize values.mp46.4 MB
  33. 4. Data preprocessing to forecast and visualize values.srt2.9 KB
  34. 5. Configure seasonality parameters in Prophet.mp45.9 MB
  35. 5. Configure seasonality parameters in Prophet.srt2.8 KB
  36. 6. How to interpret diagnostics with robust models.mp43.9 MB
  37. 6. How to interpret diagnostics with robust models.srt1.9 KB
  38. 1. Why test on unseen data during model fit.mp413.6 MB
  39. 1. Why test on unseen data during model fit.srt6.4 KB
  40. 2. Train-test split for one model.mp422.7 MB
  41. 2. Train-test split for one model.srt10.7 KB
  42. 3. Evaluate multiple models at once.mp425.7 MB
  43. 3. Evaluate multiple models at once.srt9.7 KB
  44. 1. Configure a template notebook based on new datasets.mp440.4 MB
  45. 1. Configure a template notebook based on new datasets.srt14.3 KB
  46. 1. Walk-forward validation as a more realistic choice.mp47.1 MB
  47. 1. Walk-forward validation as a more realistic choice.srt2.9 KB
  48. 2. Run a walk-forward experiment with multiple models.mp426.6 MB
  49. 2. Run a walk-forward experiment with multiple models.srt10.1 KB
  50. 3. How does TimeSeriesSplit work to produce walk-forward sets.mp413.1 MB
  51. 3. How does TimeSeriesSplit work to produce walk-forward sets.srt5.8 KB
  52. 1. Next steps.mp43.4 MB
  53. 1. Next steps.srt1.6 KB
  54. 1. Methods to visualize data with Python.mp47.8 MB
  55. 1. Methods to visualize data with Python.srt3.2 KB
  56. 2. Python libraries for data visualization.mp410.7 MB
  57. 2. Python libraries for data visualization.srt6.3 KB
  58. 3. Set Plotly as pandas backend for plotting.mp44.0 MB
  59. 3. Set Plotly as pandas backend for plotting.srt2.0 KB
  60. 4. Customize default Plotly theme.mp410.6 MB
  61. 4. Customize default Plotly theme.srt5.1 KB
  62. 5. How to interpret different plot types.mp48.5 MB
  63. 5. How to interpret different plot types.srt4.2 KB
  64. 6. Tricks to visualize multiple time series at once.mp47.9 MB
  65. 6. Tricks to visualize multiple time series at once.srt4.1 KB
  66. 1. Decomposing California solar energy using data from EIA.mp46.9 MB
  67. 1. Decomposing California solar energy using data from EIA.srt2.9 KB
  68. 2. Data preprocessing for insightful decomposition.mp415.0 MB
  69. 2. Data preprocessing for insightful decomposition.srt6.7 KB
  70. 3. Seasonal decompose with Statsmodels.mp48.9 MB
  71. 3. Seasonal decompose with Statsmodels.srt4.4 KB
  72. 4. Interpret decomposition models Additive vs. multiplicative.mp410.8 MB
  73. 4. Interpret decomposition models Additive vs. multiplicative.srt5.3 KB
  74. 5. Build DataFrame of components.mp413.9 MB
  75. 5. Build DataFrame of components.srt5.5 KB
  76. 6. Compare models using Plotly interactive visualization.mp415.9 MB
  77. 6. Compare models using Plotly interactive visualization.srt6.3 KB
  78. 1. Download US energy data using Python with EIA API.mp427.1 MB
  79. 1. Download US energy data using Python with EIA API.srt9.2 KB
  80. 2. Configure a template notebook based on new datasets.mp436.6 MB
  81. 2. Configure a template notebook based on new datasets.srt13.1 KB
  82. 3. How to specify the aggregation rule and periods.mp48.2 MB
  83. 3. How to specify the aggregation rule and periods.srt3.2 KB
  84. 4. Using Copilot to interpret a visual report with AI.mp48.9 MB
  85. 4. Using Copilot to interpret a visual report with AI.srt3.2 KB
  86. 1. Intuition behind forecasting models.mp44.8 MB
  87. 1. Intuition behind forecasting models.srt2.6 KB
  88. 2. Build DataFrame to gather forecasted future values.mp416.7 MB
  89. 2. Build DataFrame to gather forecasted future values.srt7.7 KB
  90. 3. Moving average method.mp416.9 MB
  91. 3. Moving average method.srt7.6 KB
  92. 4. Seasonal naive method.mp46.1 MB
  93. 4. Seasonal naive method.srt3.0 KB
  94. 1. Introduction to developing ARIMA models.mp47.4 MB
  95. 1. Introduction to developing ARIMA models.srt3.0 KB
  96. 2. Fit mathematical equation model.mp412.4 MB
  97. 2. Fit mathematical equation model.srt5.5 KB
  98. 3. How ARIMA changes with parameters P, D, and Q.mp45.0 MB
  99. 3. How ARIMA changes with parameters P, D, and Q.srt2.1 KB
  100. 4. Differencing to achieve stationarity.mp413.5 MB
  101. 4. Differencing to achieve stationarity.srt6.3 KB
  102. 5. ACF and PACF.mp418.2 MB
  103. 5. ACF and PACF.srt8.4 KB
  104. 6. Playground to try different configurations.mp416.9 MB
  105. 6. Playground to try different configurations.srt6.0 KB
  106. 7. Diagnostics to validate assumptions.mp424.5 MB
  107. 7. Diagnostics to validate assumptions.srt11.4 KB
  108. 8. Summary Important steps to consider in ARIMA modeling.mp47.4 MB
  109. 8. Summary Important steps to consider in ARIMA modeling.srt3.8 KB
  110. 1. Introducing seasonal order with SARIMA model.mp45.8 MB
  111. 1. Introducing seasonal order with SARIMA model.srt2.0 KB
  112. 2. Model fit and forecast.mp411.3 MB
  113. 2. Model fit and forecast.srt5.1 KB
  114. 3. Diagnostics to validate assumptions.mp45.6 MB
  115. 3. Diagnostics to validate assumptions.srt3.2 KB
  116. 4. Summary From ARIMA to SARIMA.mp46.9 MB
  117. 4. Summary From ARIMA to SARIMA.srt2.9 KB
  118. 1. How does stationarity look in a time series.mp43.0 MB
  119. 1. How does stationarity look in a time series.srt1.5 KB
  120. 2. Log transformation to achieve data stationarity.mp410.4 MB
  121. 2. Log transformation to achieve data stationarity.srt4.8 KB
  122. 3. Reverse log transformation on forecasted data.mp47.4 MB
  123. 3. Reverse log transformation on forecasted data.srt3.7 KB
  124. 4. Data transformations to achieve stationarity.mp46.2 MB
  125. 4. Data transformations to achieve stationarity.srt3.1 KB
  126. 1. Why use a metric that aggregates the residuals of a model.mp47.7 MB
  127. 1. Why use a metric that aggregates the residuals of a model.srt3.1 KB
  128. 2. Error metrics and steps to calculate.mp415.8 MB
  129. 2. Error metrics and steps to calculate.srt6.9 KB
  130. 3. Interpretation of metrics in business terms.mp47.5 MB
  131. 3. Interpretation of metrics in business terms.srt4.2 KB
  132. Bonus Resources.txt70 bytes

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