A Comparative Analysis of ARIMA, GRU, LSTM and BiLSTM on Financial Time Series Forecasting

Abstract

Machine learning and profound learning algorithms were one in every of the effective techniques to statistical prediction. Once it involves time series prediction, these algorithms shelled classic regression-based solutions in terms of accuracy. Long short-term memory (LSTM), one of the recurrent neural networks (RNN), has been incontestable to outperform typical prediction methods. The LSTM-based models are incorporated with further "gates" such that it will consider input data of longer sequences. LSTM-based models outperform Autoregressive Integrated Moving Average models attributable to these further capabilities (ARIMA). Gated-Recurrent Unit (GRU) and bidirectional long short-term memory (BiLSTM) are extended versions of LSTM. The major question is that an algorithmic program would shell the other two by giving smart predictions with minimum error. Bidirectional LSTMs provide extra training because it may be a 2-way formula, thus, it'll traverse the training information double (1. Left-to-right 2. Right-to-left). GRU has one gate below the LSTM architecture. Hence, our analysis is especially centred on that algorithm outperforms the opposite two and it conjointly deals with behavioural analysis of the algorithms, their comparison and therefore the standardization of hyper-parameters.