Abstract
Objectives: This research study presents a forecasting model that integrates an efficient discrete wavelet transform and a Backpropagation Neural Network (BPNN) for predicting financial time series. Methods/Statistical analysis: The presented model uses the wavelet transform at several time instances based on local smooth B-Spline wavelets of order d(BSd) to decompose the financial time series data. So, an approximation (long-term trends) component and several details (shortterm deviations) components are obtained. Since the details components act as a complementary part of the approximation component, to prepare a prediction model which applies all decomposed components is very advantageous. Therefore, all components are used as smooth input samples of the neural network to forecast the future of the financial time series. Findings: The proposed model is designed to forecast the stock prices of five different companies, and according to the obtained results, the presented model outperforms a conventional model that uses only the approximation component as a wavelet de-noising-based model. The numerical results have shown the prediction accuracy. Applications/Improvements: The proposed model can predict future stock prices better than the de-noised based model in nearly 70%cases. Keywords: B-Spline Wavelets Multiresolution, Back Propagation Neural Network, Financial Time Series, Stock Market Prediction
Highlights
Stock price time series analysis is one of the most considerable topics for financial researchers, and risk managers
In the matrix form of BSd, the given data vector Cj is decomposed into two parts: Cj-1(lower-resolution vector) and Dj-1, which are generated by using filters of Aj and Bj as follows: (2.2)
The proposed automated stock price time series prediction system consists of three steps: (1) The original stock price time series S is preprocessed by BSd
Summary
Stock price time series analysis is one of the most considerable topics for financial researchers, and risk managers. We have adopted a notation for representing multiresolution operations used by Samavati and Bartels, for which they had found simple matrix forms of B-Spline wavelet transform of order d used in decomposition and reconstruction of any curves and surfaces in computer graphic models[9] They have denoted {it analysis filter} matrices by Aj and Bj, and synthesis filter matrices by Pj and Qj, where the subscript j is used as the level of resolution. Vol 12 (15) | April 2019 | www.indjst.org lower-resolution, and the corresponding D0,D1,..., Dj-1, details data sets These are defined as B-Spline wavelet multiresolution of a high-resolution sample vector Cj, and the sequence C0,D0,D1, ...,Dj-1 is known as a wavelet transform[10]
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