STATISTICS Seminars Series: Gilbert Mutungi

Topic Decoding the role of time series features in LSTM forecasting: Evidence from the M4 Dataset

Abstract Long Short-Term Memory (LSTM) networks are a benchmark deep learning model for time series forecasting, yet the factors driving their performance remain unclear. We extract a comprehensive set of time series features from the M4 dataset and assess their influence on LSTM forecast accuracy. Using correlation analysis, Random Forest feature importance, and regression modeling, we evaluate their effect on forecast error. Results show that series length per forecast horizon, skewness, autocorrelation structure, and stationarity strongly predict LSTM performance, while nonlinearity and persistence have little impact. These findings clarify when LSTMs perform well and offer practical guidance for their application in forecasting tasks.