The sliding window approach in time series forecasting is a technique that structures sequential data into fixed-length input-output pairs to train models. Instead of treating the entire time series as a single sequence, the method creates smaller, overlapping windows of past observations (inputs) and corresponding future values (targets). For example, if a window size of 7 days is used, each input might contain data from days 1–7, and the target could be the value for day 8. The window then “slides” forward by one time step, so the next input includes days 2–8, and the target is day 9. This approach converts raw time series data into a supervised learning format, making it compatible with algorithms like linear regression, decision trees, or neural networks.
A key aspect of the sliding window method is its flexibility in configuring window size and forecast horizon. The window size determines how much historical data the model uses to make predictions. For instance, a weather forecasting model might use a 3-day window (temperature, humidity, etc.) to predict the next day’s temperature. Developers can also adjust the forecast horizon—the number of future steps to predict. If a model needs to predict the next 3 days, the target becomes a vector of three values instead of a single value. Additionally, the step size (how much the window shifts) is often set to 1 to maximize training examples, but larger steps reduce overlap, which can help reduce redundancy in data with high temporal correlation.
Practical implementation involves balancing trade-offs. A window too small might miss long-term trends (e.g., monthly sales patterns), while a very large window could introduce noise or computational overhead. For example, a retail sales model using a 30-day window might capture weekly seasonality but struggle with daily spikes. Preprocessing steps like normalization or handling missing data are often applied within each window. Libraries like Pandas in Python simplify window creation using methods such as shift() or rolling(). When using neural networks like LSTMs, the sliding window structure aligns with their sequence-processing capabilities—each window becomes a sequence input. This method’s simplicity and adaptability make it a foundational tool for time series tasks, from stock price prediction to energy load forecasting.