Unlock the Power of Time Series Forecasting with 80% Accuracy Boost

Time series data often exhibits patterns where past values influence future ones, especially with seasonal cycles. To enhance forecasting accuracy, incorporating lag features can significantly improve model performance. For instance, in a dataset with values [5, 10, 15, 20, 25], the value 25 at time t is preceded by 20 at t-1, 15 at t-2, and so forth. By using these lag values, models can be trained to detect and utilize these patterns, potentially increasing prediction accuracy by up to 80%. This approach leverages the inherent relationships within the data, making forecasts more reliable and insightful.

Source: towardsdatascience.com