How do you know if your time series forecasts are reliable?

1 Check the assumptions

Before you apply any forecasting method, you need to check whether your time series data meets the assumptions of the model. For example, some methods assume that the data is stationary, meaning that its mean, variance, and autocorrelation do not change over time. Other methods assume that the data has a seasonal pattern, meaning that it repeats itself at regular intervals. You can use various tests and plots to verify these assumptions, such as the Augmented Dickey-Fuller test for stationarity and the autocorrelation function plot for seasonality. If your data does not meet the assumptions, you may need to transform it or use a different method.

2 Split the data

One of the best ways to assess the reliability of your forecasts is to split your data into two sets: a training set and a test set. The training set is the part of the data that you use to fit your forecasting model, while the test set is the part of the data that you use to evaluate your forecasting model. The test set should be a representative sample of the future data that you want to predict, and it should not be used for model selection or tuning. By splitting the data, you can avoid overfitting, which is when your model fits the training data too well but fails to generalize to new data.

3 Choose the metrics

To measure the accuracy and reliability of your forecasts, you need to choose metrics that quantify the difference between the actual values and the predicted values in the test set. You can select from a variety of metrics, depending on your objective and data. Common examples include Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE). Each metric has its own benefits and drawbacks, so it's important to select the one that best fits your problem and data.

4 Compare the models

Another way to evaluate the reliability of your forecasts is to compare different forecasting models and methods, and see which one performs better on the test set. You can use the metrics that you chose to rank the models and select the best one. However, you should also consider other factors, such as the complexity, interpretability, and robustness of the models, as well as the confidence intervals and prediction intervals that they provide. Confidence intervals are the ranges that contain the true values of the parameters of the model with a certain probability, while prediction intervals are the ranges that contain the future values of the variable with a certain probability. These intervals can help you quantify the uncertainty and variability of your forecasts, and give you a sense of how reliable they are.

5 Validate the forecasts

Finally, you should validate your forecasts by comparing them with the actual data that becomes available after you make the predictions. This can help you identify any errors or biases in your forecasts, and adjust your model or method accordingly. You can also use various methods to update your forecasts as new data arrives, such as exponential smoothing or Kalman filtering. These methods can help you improve the accuracy and reliability of your forecasts over time, by incorporating the latest information and trends.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?