A learning algorithm for time series based on statistical features

In this study, we propose a machine learning technique for time-series data which combines statistical features and neural networks. The proposed algorithm is tested on various time series like stock prices, astronomical light curve and currency exchange rates. An implementation of reconstruction of unseen time series based on encodings learned by the neural network from the training data is proposed and tested. The predicted time series based on statistical features of past values show that the trained models were able to capture well the structure of the time-series data.