Financial Time Series Forecasting via CEEMDAN-LSTM with Exogenous Features

Abstract

The most recent successful time series prediction models are a combination of three elements: traditional stochastic models, machine learning models and signal processing techniques. CEEMDAN-LSTM models have combined empirical mode decomposition and long short-term memory neural networks to achieve state-of-the-art results for financial data. In this work, we propose a generalized CEEMDAN-LSTM architecture for time series forecasting capable of dealing with exogenous features as input, and the consequences of input data growth, such as convergence difficulties. Our model was applied to time series from 10 of the most liquid Brazilian stocks, and results show that accuracy is overall improved when compared to the original single feature input CEEMDAN-LSTM architecture.