Using change-point detection to support artificial neural networks for interest rates forecasting

Abstract Interest rates are one of the most closely watched variables in the economy. They have been studied by a number of researchers as they strongly affect other economic and financial parameters. Contrary to other chaotic financial data, the movement of interest rates has a series of change points owing to the monetary policy of the US government. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in interest rates forecasting. The proposed model consists of three stages. The first stage is to detect successive change points in the interest rates dataset. The second stage is to forecast the change-point group with the backpropagation neural network (BPN). The final stage is to forecast the output with BPN. This study then examines the predictability of the integrated neural network model for interest rates forecasting using change-point detection.

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