outofsamplejaksoihin

outofsamplejaksoihin is a Finnish term that translates to "out-of-sample periods" in English. It refers to the time intervals or data points that are not used during the training or development of a statistical model, machine learning algorithm, or forecasting system. The primary purpose of out-of-sample periods is to provide an unbiased evaluation of the model's performance and its ability to generalize to new, unseen data.

When building predictive models, it is crucial to split the available data into at least two sets: a training set and a testing set. The training set is used to fit the model's parameters, allowing it to learn patterns and relationships from the data. The out-of-sample period, typically represented by the testing set, is then used to assess how well the trained model performs on data it has never encountered before. This process helps to detect issues like overfitting, where a model performs exceptionally well on the training data but poorly on new data due to memorizing specific training examples rather than learning generalizable patterns.

The evaluation conducted on out-of-sample periods is vital for determining the real-world applicability and reliability of a model. Metrics such as accuracy, precision, recall, or mean squared error are calculated using the predictions made on the out-of-sample data. A model that performs well on out-of-sample data is considered to have good generalization capabilities. In time series analysis, out-of-sample periods often refer to future time steps that are forecasted. The accuracy of these forecasts is then evaluated once the actual values for those future periods become available.