Lagged variables in time series forecasting refer to previous observations of a variable that are used as predictors in a model. Essentially, these variables represent the values of the target variable from earlier time periods. For instance, if trying to forecast the sales of a product for the upcoming month, you might consider the sales data from the previous months as lagged variables. In this case, the sales figures from one month ago, two months ago, and so forth would serve as lagged variables that help to identify patterns and trends over time.
Using lagged variables is an effective way to capitalize on the temporal nature of the data. Time series data is often autocorrelated, meaning that current values are correlated with past values. By including these lagged variables in your forecasting model, you can enhance its predictive power. For example, in a simple linear regression model, you might include the most recent three months of sales data as inputs. This way, the model can learn how past sales affect future sales, thus improving the accuracy of its predictions.
Multiple lagged variables can be included to account for different time intervals, such as using monthly, quarterly, or yearly data. It’s also common to create new features based on lagged data, such as the moving average of sales over the past three months. This can help smooth out short-term fluctuations and highlight longer-term trends. Overall, lagged variables are a fundamental component of time series analysis, helping developers build models that are more predictive and robust to the patterns present in historical data.