Time-Series-Based Classification of Financial Forecasting Discrepancies

Abstract

We aim to classify financial discrepancies between actual and forecasted performance into categories of commentaries that an analyst would write when describing the variation. We propose analyzing time series in order to perform the classification. Two time series classification algorithms – 1-nearest neighbour with dynamic time warping (1-NN DTW) and time series forest – and long short-term memory (LSTM) networks are compared to common machine learning algorithms. We investigate including supporting datasets such as customer sales data and inventory. We apply data augmentation with noise as an alternative to random oversampling. We find that LSTM and 1-NN DTW provide the best results. Including sales data has no effect but inventory data improves the predictive power of all models examined. Data augmentation has a slight improvement for some models over random oversampling.