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Backtesting with Skforecast: Time Series Forecasting in Python
Comprehensive Guide to Backtesting with Skforecast: Ensuring Reliable Time Series Forecasting in Python
Introduction to Backtesting in Forecasting
This is a comprehensive guide to backtesting with skforecast in python with examples. Backtesting in time series forecasting simulates how a model would perform if it had been applied in the past, based on historical data. This process is crucial for validating models before deployment, as it offers insights into potential forecasting errors and helps optimize model performance. By testing how models perform under different scenarios, forecasters can better understand limitations and avoid overfitting.
For instance, backtesting allows you to assess how a model might perform during different seasonal cycles or economic conditions, enabling a more robust evaluation. In this guide, we’ll leverage Skforecast, a Python library that integrates seamlessly with scikit-learn, to facilitate backtesting for time series models.
