FINANCIAL TIME SERIES FORECASTING BY NEURAL NETWORK USING CONJUGATE GRADIENT LEARNING ALGORITHM AND MULTIPLE LINEAR REGRESSION WEIGHT INITIALIZATION

Abstract

Multilayer neural network has been successfully applied to the time series forecasting. Backpropagation, a popular learning algorithm, converges slowly and has the difficulty in determining the network parameters. In this paper, conjugate gradient learning algorithm with restart procedure is introduced to overcome these problems. Also, the commonly used random weight initialization does not guarantee to generate a set of initial connection weights close to the optimal weights leading to slow convergence. Multiple linear regression (MLR) provides an alternative for weight initialization. The daily trade data of the listed companies from Shanghai Stock Exchange is collected for technical analysis with the means of neural networks. Two learning algorithms and two weight initializations are compared. The results find that neural networks can model the time series satisfactorily. The proposed conjugate gradient with MLR weight initialization requires a lower computation cost and learns better than backpropagation with random initialization.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 55 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.