Implementation Code [A-novel-validation-framework-to-enhance-deep-learning-models in time-series forecasting]
Implementation code of article: A novel validation framework to enhance deep learning models in time-series forecasting
In this work, we introduced a novel framework for supporting deep learning in enhancing accurate, efficient and reliable time-series models. The major novelty of our proposed methodology is that it ensures a time-series to be "suitable" for fitting a deep learning model by performing a series transformations in order to satisfy the stationarity property. - The enforcement of stationarity is performed by the application of Augmented Dickey-Fuller test and transformations based on first differences or returns, without the loss of any embedded information. The reliability of the deep learning model's predictions is guaranteed by rejecting the hypothesis of auto-correlation in the model's errors, which is demonstrated by autocorrelation function plots, Ljung-Box Q-test and Box-Pierce test. - The performance of the forecasting model is evaluated on both problems of forecasting time-series price (regression) and time-series directional movements (classification).