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

Understand

Machine and deep learning-based algorithms are the emerging approaches in addressing prediction problems in time series.

• These techniques have been shown to produce more accurate results than conventional regression-based modeling.
• It has been reported that artificial Recurrent Neural Networks (RNN) with memory, such as Long Short-Term Memory (LSTM), are superior compared to Autoregressive Integrated Moving Average (ARIMA) with a large margin.
• The LSTM-based models incorporate additional "gates" for the purpose of memorizing longer sequences of input data.