Time series forecasting and regression are both techniques used to predict outcomes, but they differ in how they handle data and model relationships. The key distinction lies in the role of time and the structure of the data. Time series forecasting explicitly models temporal dependencies, where the order of data points matters and predictions depend on past observations. Regression, however, focuses on identifying relationships between variables, regardless of their sequence. For example, predicting tomorrow’s temperature using historical weather data is a time series problem, while estimating a house’s price based on its size and location is a regression task.
The data structure and assumptions differ significantly. Time series data is sequential and often exhibits trends, seasonality, or autocorrelation (where past values influence future ones). Models like ARIMA (Autoregressive Integrated Moving Average) explicitly account for these patterns by using lagged values or differencing to stabilize trends. Regression models, such as linear regression, assume independence between observations and focus on correlations between input features and the target. For instance, a regression model predicting sales might use variables like marketing spend and product price, while a time series model would analyze how sales change over weeks or months, capturing recurring patterns like holiday spikes.
Another difference is in evaluation and application. Time series forecasting often requires specialized validation techniques, such as walk-forward testing, where the model is trained on historical data and tested on subsequent periods to mimic real-world forecasting. Metrics like MAE (Mean Absolute Error) or RMSE (Root Mean Squared Error) are common in both approaches, but time series may also use metrics like MASE (Mean Absolute Scaled Error) to compare against naive forecasts. Regression tasks, in contrast, might prioritize R-squared to measure how well features explain variance. Misapplying regression to time series data—for example, ignoring temporal order—can lead to misleading results, such as underestimating future sales if seasonal trends aren’t modeled. Developers must choose the right tool based on whether the problem inherently depends on time or general feature relationships.