Composition of Feature Selection for Time-Series Prediction with Deep Learning.

The concept of predictions has gained much attention over the last few years. Research on prediction based on experience is error-prone. Usually, a lot of data has been available with multiple variables, but not all features are relevant. This work uses different feature selection techniques and their combinations to select the best possible feature subset. This paper uses feature selection techniques for time series data prediction with deep learning models. In this work, we have used filter, wrapper, and embedded methods for feature selection. We combine these techniques and compare their performance with single feature selection techniques. We found that the embedded method alone performs well with fewer features, but its performance deteriorates when the number of features increases. We input these reduced feature sets into deep learning models such as recurrent neural networks (RNN), long short-term memory (LSTM), and 1d Convolutional with LSTM (1d CNN-LSTM). We assess the performance of different deep-learning models with reduced features. Our results analysis found that the right feature subset may help increase the performance of deep learning models for time series prediction. Performance improvement is specific to the dataset, feature selection techniques, and the deployed DL models; performance improvement varies on average, from a few percent to fifty percent. [ABSTRACT FROM AUTHOR]