The complete pipeline
A time series forecasting project always follows the same pipeline: exploration, preprocessing, model selection and training, evaluation, deployment and monitoring.
Step 1: exploration and diagnostics
Visualise your data first. Identify trend, seasonality (annual, weekly, daily), outliers and structural breaks. Test stationarity with the ADF test.
Step 2: choose the right model
Prophet (Meta): our default recommendation
Prophet is robust, easy to configure and natively handles missing data, holidays and trend changes. Our starting point for 80% of sales forecasting cases.
LightGBM with temporal features
For series with many explanatory variables (weather, price, promotions), gradient boosting with temporal features (lag variables, rolling averages) often outperforms classical models.
Anti-pattern to avoid: never train on future data. Train/test split on time series must respect chronological order. Use TimeSeriesSplit from scikit-learn.
Step 3: correct evaluation
MAPE for comparison between different volume products. MAE for absolute average error. RMSE to penalise large errors.
Production with confidence intervals
Always communicate confidence intervals in production, not just a point forecast. '1,200 units with 90% probability in range [950, 1,450]' is infinitely more useful than simply '1,200'.
Time series and forecasting are part of the Python block of the Data Analyst Bootcamp.
