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1. Configure a template notebook based on new datasets.mp4
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MP4
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40.4 MB
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1. Configure a template notebook based on new datasets.srt
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SRT
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14.3 KB
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1. Decomposing California solar energy using data from EIA.mp4
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MP4
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6.9 MB
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1. Decomposing California solar energy using data from EIA.srt
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SRT
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2.9 KB
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1. Download US energy data using Python with EIA API.mp4
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MP4
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27.1 MB
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1. Download US energy data using Python with EIA API.srt
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SRT
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9.2 KB
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1. How does stationarity look in a time series.mp4
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MP4
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3 MB
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1. How does stationarity look in a time series.srt
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SRT
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1.5 KB
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1. Introducing seasonal order with SARIMA model.mp4
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MP4
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5.8 MB
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1. Introducing seasonal order with SARIMA model.srt
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SRT
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2 KB
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1. Introduction to Prophet A semi-automatic time series model.mp4
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MP4
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6.7 MB
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1. Introduction to Prophet A semi-automatic time series model.srt
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SRT
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2.8 KB
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1. Introduction to developing ARIMA models.mp4
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MP4
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7.4 MB
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1. Introduction to developing ARIMA models.srt
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SRT
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3 KB
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1. Intuition behind forecasting models.mp4
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MP4
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4.8 MB
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1. Intuition behind forecasting models.srt
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SRT
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2.6 KB
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1. Methods to visualize data with Python.mp4
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MP4
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7.8 MB
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1. Methods to visualize data with Python.srt
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SRT
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3.2 KB
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1. Next steps.mp4
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MP4
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3.4 MB
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1. Next steps.srt
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SRT
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1.6 KB
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1. SARIMA vs. exponential smoothing.mp4
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MP4
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3.5 MB
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1. SARIMA vs. exponential smoothing.srt
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SRT
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1.9 KB
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1. Search and download Federal Reserve Economic Data.mp4
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MP4
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4.5 MB
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1. Search and download Federal Reserve Economic Data.srt
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SRT
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1.9 KB
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1. Walk-forward validation as a more realistic choice.mp4
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MP4
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7.1 MB
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1. Walk-forward validation as a more realistic choice.srt
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SRT
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2.9 KB
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1. Why learn practical Python for time series forecasting.mp4
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MP4
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3.8 MB
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1. Why learn practical Python for time series forecasting.srt
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SRT
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1 KB
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1. Why test on unseen data during model fit.mp4
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MP4
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13.6 MB
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1. Why test on unseen data during model fit.srt
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SRT
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6.4 KB
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1. Why use a metric that aggregates the residuals of a model.mp4
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MP4
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7.7 MB
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1. Why use a metric that aggregates the residuals of a model.srt
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SRT
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3.1 KB
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2. Build DataFrame to gather forecasted future values.mp4
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MP4
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16.7 MB
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2. Build DataFrame to gather forecasted future values.srt
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SRT
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7.7 KB
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2. Configure a template notebook based on new datasets.mp4
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MP4
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36.6 MB
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2. Configure a template notebook based on new datasets.srt
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SRT
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13.1 KB
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2. Data preprocessing for insightful decomposition.mp4
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MP4
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15 MB
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2. Data preprocessing for insightful decomposition.srt
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SRT
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6.7 KB
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2. Error metrics and steps to calculate.mp4
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MP4
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15.8 MB
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2. Error metrics and steps to calculate.srt
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SRT
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6.9 KB
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2. Fit mathematical equation model.mp4
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MP4
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12.4 MB
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2. Fit mathematical equation model.srt
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SRT
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5.5 KB
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2. How to use Codespaces.mp4
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MP4
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9.2 MB
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2. How to use Codespaces.srt
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SRT
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4.6 KB
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2. Load CSV and set dtype as datetime.mp4
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MP4
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12.6 MB
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2. Load CSV and set dtype as datetime.srt
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SRT
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6.8 KB
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2. Log transformation to achieve data stationarity.mp4
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MP4
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10.4 MB
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2. Log transformation to achieve data stationarity.srt
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SRT
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4.8 KB
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2. Model fit and forecast.mp4
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MP4
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11.3 MB
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2. Model fit and forecast.srt
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SRT
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5.1 KB
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2. Model fit step by step.mp4
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MP4
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16.8 MB
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2. Model fit step by step.srt
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SRT
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7.3 KB
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2. Python libraries for data visualization.mp4
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MP4
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10.7 MB
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2. Python libraries for data visualization.srt
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SRT
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6.3 KB
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2. Run a walk-forward experiment with multiple models.mp4
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MP4
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26.6 MB
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2. Run a walk-forward experiment with multiple models.srt
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SRT
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10.1 KB
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2. Train-test split for one model.mp4
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MP4
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22.7 MB
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2. Train-test split for one model.srt
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SRT
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10.7 KB
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3. Datetime components on different columns.mp4
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MP4
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2.4 MB
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3. Datetime components on different columns.srt
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SRT
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1.4 KB
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3. Diagnostics to validate assumptions.mp4
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MP4
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5.6 MB
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3. Diagnostics to validate assumptions.srt
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SRT
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3.2 KB
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3. Evaluate multiple models at once.mp4
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MP4
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25.7 MB
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3. Evaluate multiple models at once.srt
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SRT
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9.7 KB
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3. Feed holidays data into the model.mp4
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MP4
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5.8 MB
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3. Feed holidays data into the model.srt
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SRT
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2.4 KB
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3. How ARIMA changes with parameters P, D, and Q.mp4
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MP4
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5 MB
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3. How ARIMA changes with parameters P, D, and Q.srt
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SRT
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2.1 KB
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3. How does TimeSeriesSplit work to produce walk-forward sets.mp4
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MP4
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13.1 MB
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3. How does TimeSeriesSplit work to produce walk-forward sets.srt
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SRT
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5.8 KB
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3. How to specify the aggregation rule and periods.mp4
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MP4
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8.2 MB
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3. How to specify the aggregation rule and periods.srt
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SRT
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3.2 KB
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3. Interpretation of metrics in business terms.mp4
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MP4
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7.5 MB
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3. Interpretation of metrics in business terms.srt
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SRT
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4.2 KB
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3. Moving average method.mp4
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MP4
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16.9 MB
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3. Moving average method.srt
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SRT
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7.6 KB
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3. Reverse log transformation on forecasted data.mp4
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MP4
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7.4 MB
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3. Reverse log transformation on forecasted data.srt
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SRT
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3.7 KB
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3. Seasonal decompose with Statsmodels.mp4
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MP4
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8.9 MB
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3. Seasonal decompose with Statsmodels.srt
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SRT
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4.4 KB
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3. Set Plotly as pandas backend for plotting.mp4
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MP4
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4 MB
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3. Set Plotly as pandas backend for plotting.srt
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SRT
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2 KB
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3. Understand model configurations based on playground.mp4
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MP4
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8.4 MB
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3. Understand model configurations based on playground.srt
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SRT
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3.8 KB
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4. Customize default Plotly theme.mp4
|
MP4
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10.6 MB
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4. Customize default Plotly theme.srt
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SRT
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5.1 KB
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4. Data preprocessing to forecast and visualize values.mp4
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MP4
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6.4 MB
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4. Data preprocessing to forecast and visualize values.srt
|
SRT
|
2.9 KB
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4. Data transformations to achieve stationarity.mp4
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MP4
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6.2 MB
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4. Data transformations to achieve stationarity.srt
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SRT
|
3.1 KB
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4. Diagnostics to validate assumptions and inform model choice.mp4
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MP4
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7.7 MB
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4. Diagnostics to validate assumptions and inform model choice.srt
|
SRT
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3.6 KB
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4. Differencing to achieve stationarity.mp4
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MP4
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13.5 MB
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4. Differencing to achieve stationarity.srt
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SRT
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6.3 KB
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4. Interpret decomposition models Additive vs. multiplicative.mp4
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MP4
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10.8 MB
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4. Interpret decomposition models Additive vs. multiplicative.srt
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SRT
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5.3 KB
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4. Seasonal naive method.mp4
|
MP4
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6.1 MB
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4. Seasonal naive method.srt
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SRT
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3 KB
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4. Summary From ARIMA to SARIMA.mp4
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MP4
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6.9 MB
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4. Summary From ARIMA to SARIMA.srt
|
SRT
|
2.9 KB
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4. Using Copilot to interpret a visual report with AI.mp4
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MP4
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8.9 MB
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4. Using Copilot to interpret a visual report with AI.srt
|
SRT
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3.2 KB
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4. Why set the datetime column as index.mp4
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MP4
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8.4 MB
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4. Why set the datetime column as index.srt
|
SRT
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4.9 KB
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5. ACF and PACF.mp4
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MP4
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18.2 MB
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5. ACF and PACF.srt
|
SRT
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8.4 KB
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5. Build DataFrame of components.mp4
|
MP4
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13.9 MB
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5. Build DataFrame of components.srt
|
SRT
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5.5 KB
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5. Configure seasonality parameters in Prophet.mp4
|
MP4
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5.9 MB
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5. Configure seasonality parameters in Prophet.srt
|
SRT
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2.8 KB
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5. How to interpret different plot types.mp4
|
MP4
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8.5 MB
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5. How to interpret different plot types.srt
|
SRT
|
4.2 KB
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5. Load and preprocess data from Excel.mp4
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MP4
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5.6 MB
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5. Load and preprocess data from Excel.srt
|
SRT
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3.4 KB
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6. Compare models using Plotly interactive visualization.mp4
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MP4
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15.9 MB
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6. Compare models using Plotly interactive visualization.srt
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SRT
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6.3 KB
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6. How to interpret diagnostics with robust models.mp4
|
MP4
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3.9 MB
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6. How to interpret diagnostics with robust models.srt
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SRT
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1.9 KB
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6. Playground to try different configurations.mp4
|
MP4
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16.9 MB
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6. Playground to try different configurations.srt
|
SRT
|
6 KB
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6. Tricks to visualize multiple time series at once.mp4
|
MP4
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7.9 MB
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6. Tricks to visualize multiple time series at once.srt
|
SRT
|
4.1 KB
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7. Diagnostics to validate assumptions.mp4
|
MP4
|
24.5 MB
|
|
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7. Diagnostics to validate assumptions.srt
|
SRT
|
11.4 KB
|
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8. Summary Important steps to consider in ARIMA modeling.mp4
|
MP4
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7.4 MB
|
|
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8. Summary Important steps to consider in ARIMA modeling.srt
|
SRT
|
3.8 KB
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Bonus Resources.txt
|
TXT
|
102.4 B
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|
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Get Bonus Downloads Here.url
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URL
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204.8 B
|
