AI for Time Series Forecasting
Learn how AI models predict future values from time series data — ARIMA, Prophet, LSTMs, and Transformers for temporal data. What is Time Series Forecasting? Time series forecasting predicts future values based on historical temporal data. Applications include stock price prediction, weather forecasting, demand planning, and resource allocation. Unlike other ML tasks, time series has temporal dependency — the order of observations matters. Classical Approaches ARIMA (AutoRegressive Integrated Moving Average) is the traditional statistical method. It models trends, seasonality, and residuals. Prophet (by Facebook/Meta) handles holidays and changepoints automatically. These methods work well with limited data and provide interpretable results with confidence intervals. Deep Learning for Time Series LSTMs (Long Short-Term Memory networks) capture long-range dependencies in sequential data. They can model complex non-linear patterns. CNN-based approaches (TCN, WaveNet) offer faster training with dilated convolutions. The Transformer architecture with positional encoding has become state-of-the-art for long sequence forecasting (Informer, Autoformer). Best Practices Always split time series data chronologically — never randomly. Use walk-forward validation where the model is repeatedly retrained on expanding windows. Engineer features: lag values, rolling statistics, time-based features (day of week, month, holiday flags). Handle missing values carefully — forward fill for short gaps, interpolation for longer ones. Scale data per series, not globally, when working with multiple time series.