Empirical Mode Decomposition with Envelope Extraction and LSTM for univariate time series forecasting
Mohamed NDIAYE, Mamadou Mboup · 2023
This paper proposes a forecasting method for uni-variate time series based on a combination of Long-Short-Term-Memory (LSTM) Neural Network and Empirical Mode Decomposition (EMD) with envelope extraction. The basic steps of the method are 1) to split each Intrinsic Mode Function (IMF) into its envelope and detail, 2) to forecast separately these two components for each IMF, using LSTM and 3) to recombine the different forecasts. As already proven, the use of EMD in a forecast setting improves the accuracy. In this paper, we show that the splitting step on each IMF provides further significant improvement on the forecast. This is developed to be applied to treasury transactions forecast. The method relies on well known and easily implementable techniques and is usable with most forecasting algorithms. We experimented it on 3 non-stationary time series, showing its portability to a wide variety of univariate time series.