Parameter
Tuning of Neural Network for Financial Time Series Forecasting
Zeinab Fallahshojaei1 and
Mehdi Sadeghzadeh2
1Department
of Computer Engineering, Buin Zahra Branch, Islamic Azad University, Buin Zahra,
Iran
2Department of Computer Engineering, Mahshahr Branch, Islamic Azad
University, Mahshahr, Iran
Abstract:
One of the most challengeable problems in pattern recognition
domain is financial time series forecasting which aims to exactly estimate the
cost value variations of a particular object in future. One of the best
well-known financial time series prediction methods is Neural Network (NN) but
it suffers from parameter tuning such as number of neuron in hidden layer,
learning rate and number of periods that should be forecasted. To solve the problem,
this paper proposes a new meta-heuristic-based parameter tuning scheme which is
based on Harmony Search (HS). To improve the exploration and exploitation rates
of HS, the control parameters of HS are adapted during the generations. Evaluation
of the proposed method on several financial times series datasets shows the
efficiency of the improved HS on parameter setting of NN for time series
prediction.
Keywords: Financial
times series forecasting, parameter setting, NN, HS, parameter adaptation.