Question

which of the following is not a step in cross-validation with time series? multiple choice question. split the series into early and later periods, representing the training and the validation sets, respectively use the entire data set, one that combines the training and the validation sets, to estimate the preferred model for making forecasts. ideally, the preferred model will have the highest mse, mad, and mape values. explore suitable forecasting models for the training set and use the forecast errors in the validation set to compute mse, mad, and mape.

Answer 1

In time series cross-validation, the incorrect step is to aim for the highest MSE, MAD, and MAPE values; instead, the objective is to minimize these error metrics. The goal is to identify a forecasting model with the lowest error measurements for reliable predictions.

Step-by-step explanation:

The question concerns the process of cross-validation in time series analysis, a critical step in ensuring the robustness of predictive models. In the context of time series analysis, cross-validation differs from other data domains due to the sequential nature of the data.

The incorrect step in time series cross-validation is the following: ideally, the preferred model will have the highest MSE, MAD, and MAPE values. This statement is false because the goal of a good forecasting model is to minimize error, so you'd want the lowest possible values for Mean Squared Error (MSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE), not the highest.

The correct steps would involve splitting the time series data, exploring various forecasting models on the training set, and then evaluating their performance on the validation set using error metrics like MSE, MAD, and MAPE to select the model with the lowest error measurements.