Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting

Abstract

Modeling and analysis of time series are important in applications including economics, engineering, environmental science and social science. Selecting the best time series model with accurate parameters in forecasting is a challenging objective for scientists and academic researchers. Hybrid models combining neural networks and traditional Autoregressive Moving Average (ARMA) models are being used to improve the accuracy of modeling and forecasting time series. Most of the existing time series models are selected by information-theoretic approaches, such as AIC, BIC, and HQ. This paper revisits a model selection technique based on Minimum Message Length (MML) and investigates its use in hybrid time series analysis. MML is a Bayesian information-theoretic approach and has been used in selecting the best ARMA model. We utilize the long short-term memory (LSTM) approach to construct a hybrid ARMA-LSTM model and show that MML performs better than AIC, BIC, and HQ in selecting the model—both in the traditional ARMA models (without LSTM) and with hybrid ARMA-LSTM models. These results held on simulated data and both real-world datasets that we considered.We also develop a simple MML ARIMA model.

Regional Economic Forecasting Method Based on Recurrent Neural Network

“…GRU. Recurrent neural network (RNN) is a neural network structure for sequential data, the core of which is to recycle the parameters of network layers to avoid the parameter surge caused by the increase of time step, and to introduce the hidden state for recording historical information to effectively deal with the before and after correlation of data [25][26][27][28]. RNN is very effective for data with sequential characteristics, and it can mine the temporal information as well as semantic information in the data.…”