Accounting journal entries as a long-term multivariate time series: Forecasting wholesale warehouse output

Abstract

Less than 2 years ago, many small entrepreneurs in the commodities trading business faced price volatility, which had not been the case for the last few decades. Generally, the income section of the profit and loss statement was not the main problem, especially in building material commodities trading, due to the recent growth in real estate demand. Logistic disorders, raw material shortages, inflation, and interest rate growth caused difficulties in supply management and warehouse balancing, which were reflected in a particular significant expense called the cost of goods sold. The real problem of its forecasting was identified, and data from accounting books likely contain information about previous warehouse dynamics. This paper presents how accounting data are prepared and shaped into time series suitable for machine learning algorithms, the relevant literature that helped in algorithm selection, and the development and description of the forecasting model, as well as its benchmarking with traditional forecasting models. Visualization and mean squared error loss measured on unseen data show that the model has proven more successful than expected. Based on data from four journal accounts spanning over 14 years, the model predicts the debit and credit sides of the wholesale warehouse for 150 working days.