A comparison of Local and Global approaches to time series forecasting, with a Python demonstration using LightGBM and the Australian Tourism dataset.
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What is Local forecasting?
Local forecasting is the traditional approach where we train one predictive model for each time series independently. The classical statistical models (like exponential smoothing, ARIMA, TBATS, etc.) typically use this approach, but it can also be used by standard machine learning models via a feature engineering step.
Local forecasting has advantages:
- It’s intuitive to understand and implement.
- Each model can be tweaked separately.
But it also has some limitations:
- It suffers from the “cold-start” problem: it requires a relatively large amount of historical data for each time series to estimate the model parameters reliably. It also makes it impossible to predict new targets, like the demand for a new product.
- It can’t capture the commonalities and dependencies among related time series, like cross-sectional or hierarchical relationships.
- It’s hard to scale to large datasets with many time series, as it requires fitting and maintaining a separate model for each target.
What is Global forecasting?
Global forecasting is a more modern approach, where multiple time series are used to train a single “global” predictive model. By doing so, it has a larger training set and it can leverage shared structures across the targets to learn complex relations, ultimately leading to better predictions.
Building a global forecasting model typically involves a feature engineering step to build features like:
- Lagged values of the target
- Statistics of the target over time-windows (e.g…
