Periodic Time Series Forecasting with Bidirectional Long Short-Term Memory

Deep learning methods such as recurrent neural network and long short-term memory have recently drawn a lot of attentions in many fields such as computer vision, natural language processing and finance. Long short-term memory is a type of recurrent neural network capable of predicting future values of sequential data by learning observed data over time. Many real-world time series in business, finance, weather forecasting and engineering science have periodic property like daily, monthly, quarterly or yearly period and need efficient tools to forecast their future events and values. The forecasting study and tools in these fields are therefore essential and important. In this paper, we present a deep learning technique, called bidirectional long short-term memory, in forecasting time series data. The bidirectional long short-term memory model is evaluated based on the benchmark periodic time series dataset. The model performs well on the macro and industry categories and achieves average mean absolute percentage errors less than 9%. It is shown that the bidirectional architecture obtains the better results than the baseline models. We also test the model by tuning the time step parameter to evaluate how the time step length impacts on forecasting performance of the model.