Time Series Forecasting Tutorial
Learn to forecast with Python: dates in pandas, trend and seasonality, baselines, exponential smoothing by hand, ARIMA, lag features and festival peaks, all in your browser.
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Getting Started Intermediate
Dates & Time Indexes Intermediate
Resampling & Rolling Windows Intermediate
- 6Resampling: Daily to Weekly & Monthly (coming soon)
- 7Rolling Windows (coming soon)
- 8Plotting Time Series (coming soon)
Patterns in a Series Intermediate
- 9Trend, Seasonality & Noise (coming soon)
- 10Decomposition (coming soon)
- 11Stationarity (coming soon)
- 12Differencing & Log Transforms (coming soon)
- 13Autocorrelation (ACF) (coming soon)
- 14Partial Autocorrelation (PACF) (coming soon)
Measuring Forecasts Advanced
- 15Error Metrics: MAE, RMSE & MAPE (coming soon)
- 16Train/Test Split by Time (coming soon)
- 17Walk-Forward Validation (coming soon)
Baselines & Smoothing Advanced
- 18Naive & Seasonal Naive Baselines (coming soon)
- 19Moving Average Forecasts (coming soon)
- 20Simple Exponential Smoothing by Hand (coming soon)
- 21Holt's Method: Adding Trend (coming soon)
- 22Holt-Winters: Adding Seasonality (coming soon)
ARIMA Models Advanced
- 23AR & MA Models (coming soon)
- 24ARIMA with statsmodels (coming soon)
- 25Seasonal ARIMA (SARIMA) (coming soon)
Machine Learning Forecasting Advanced
- 26Lag & Calendar Features (coming soon)
- 27Forecasting with scikit-learn (coming soon)
- 28Multi-Step Forecasts (coming soon)
- 29Holidays, Festivals & Monsoon Effects (coming soon)
- 30Prediction Intervals (coming soon)
Project Project
- 31Project: Monthly Demand Forecast for a Shop (coming soon)
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