Multi-stage approach with DTW and clustering for forecasting of average deposit rate in Ukraine

Abstract

The paper is dedicated to the development of the multi-stage forecasting method that is based on Dynamic Time Warping, Clustering and AutoARIMA techniques, which is compared with several traditional benchmarks on the unique dataset. The goal is to forecast an average deposit rate in Ukraine using data that has been scrapped from banks' websites about their individual deposit rates on the daily basis. From this rich dataset the paper focuses only on 12-month deposits, UAH, for each bank. Most of the issues that are traditional for web-scraping approach are irrelevant in our case due to the dataset features. These rates are aggregated into groups by similarity in dynamics, forecasted separately with an AutoARIMA routine and finally aggregated into the entire forecast using weights that have been obtained with an OLS estimation. The paper presents the result and comparison with several benchmarks, starting from simple Random Walk, a few specifications of ARIMA and simple Random Forest. The multi-stage approach outperforms benchmarks by an RMSE and graphical analysis over the latter period of the data.