What is the difference between causal forecasting and time series forecasting?

Causal forecasting is a technique used to project future outcomes based on the relationship between the outcome variable and one or more independent variables (causal factors). It assumes that the outcome is influenced by certain variables, and the relationship between these variables and the outcome can be measured and used to make predictions.

Time series forecasting is an approach that uses historical data to predict future outcomes. This method assumes that patterns and trends observed in past data can be used to forecast future values. Time series models rely on techniques such as moving averages, exponential smoothing, and autoregressive integrated moving average (ARIMA) models to analyze the historical data and make predictions.

One key difference between causal forecasting and time series forecasting is the data input. Causal forecasting models require data on both the outcome variable and the independent variables that are believed to influence it. Time series forecasting, on the other hand, only requires a historical sequence of data points for the outcome variable.

Causal forecasting assumes that the outcome is driven by specific independent variables, and the relationship between the outcome and these variables can be quantified. Time series forecasting assumes that historical patterns and trends in the outcome variable will continue in the future.

Causal forecasting focuses on identifying the factors that influence the outcome variable and quantifying their impact. It is more concerned with understanding the relationships between variables. Time series forecasting is focused on identifying patterns and trends in the historical data and is less concerned with uncovering the underlying causal mechanisms.

Causal forecasting is applicable when there is a known and measurable relationship between the outcome variable and one or more independent variables. It can be more flexible than time series forecasting by allowing for the inclusion of additional causal variables as they become available or relevant. Time series forecasting is most applicable when you have a long history of data points for the outcome variable and believe that the patterns observed in the data will continue into the future. It can be less flexible, as it relies purely on historical data and does not account for external factors that might impact the outcome variable. By following these steps and discussing the key differences between causal forecasting and time series forecasting, you can understand the distinction between these two forecasting methods and choose the appropriate technique based on the given data and problem situation.