A Low Complexity FLANN Architecture for Forecasting Stock Time Series Data Training with Meta-Heuristic Firefly Algorithm

Abstract

Prediction of future trends in financial time-series data related to the stock market is very important for making decisions to make high profit in the stock market trading. Typically the economic time-series data are non-linear, volatile and many other factors crash the market for local or global issues. Because of these factors investors find difficult to predict consistently and efficiently. The motive of designing a framework for predicting time series data is by using a low complexity, adaptive functional link artificial neural network (FLANN). The FLANN is basically a single layer structure in which non-linearity is introduced by enhancing the input pattern. The architecture of FLANN is trained with Meta-Heuristic Firefly Algorithm to achieve the excellent forecasting to increase the accurateness of prediction and lessen in training time. The projected framework is compared by using FLANN training with conventional back propagation learning method to examine the accuracy of the model.