flâneur

Time Series Evaluation Metrics: MAE, MSE, RMSE, MAPE

apxml.com · 1,150 words · saved by 1 readers

After a forecasting model, such as ARIMA or SARIMA, has been trained, its performance needs to be quantified. Simply looking at plots is often insufficient for objective comparison or reporting. Evaluation metrics provide a standardized way to measure how closely the model's forecasts align with the actual observed values in your test dataset. These metrics focus on the prediction errors, which are the differences between the actual values ( 𝐴 𝑐 𝑡 𝑢 𝑎 𝑙 𝑖 Actual i ​ ) and the forecasted values ( 𝐹 𝑜 𝑟 𝑒 𝑐 𝑎 𝑠 𝑡 𝑖 Forecast i ​ ) at each time step 𝑖 i. Let's examine four common metrics used in time series forecasting. The Mean Absolute Error, or 𝑀 𝐴 𝐸 MAE, represents the average absolute difference between the forecasts and the actual values. It tells you, on average, how far off your predictions are from the real outcomes, ignoring the direction of the error (whether you predicted too high or too low). The formula is: 𝑀 𝐴 𝐸 = 1 𝑛 ∑ 𝑖 = 1 𝑛 ∣ 𝐴 𝑐 𝑡 𝑢

After a forecasting model, such as ARIMA or SARIMA, has been trained, its performance needs to be quantified. Simply looking at plots is often insufficient for objective comparison or reporting. Evaluation metrics provide a standardized way to measure how closely the model's forecasts align with the actual observed values in your test dataset. These metrics focus on the prediction errors, which are the differences between the actual values (ActualiActual_i) and the forecasted values (ForecastiForecast_i) at each time step ii. Let's examine four common metrics used in time series…

saved by

related reading