Beschreibung
Simple and clear discussion of financial time series prediction and its complexity. Predictability of future behavior of the stock market. Proposing some hybrid adaptive neuro-evolutionary forecasting models. Forecasting both short as well as long term. Experimenting global stock market data. Emphasis on data pre-processing. Statistical significance test of the results obtained from extensive simulation work. Clear model architecture and training algorithm. Pictorial description of the simulated results obtained from real stock data. Some of the functions/codes in matalb of which are used in numerical experiments. Proposing evolutionary virtual data position exploration and incorporation to the financial time series in order to improve the forecasting accuracy of the models.
Autorenporträt
Sarat Chandra Nayak holds a Ph.D. in Computer Engineering. His research interests are Predictive Systems, Financial Time Series Forecasting, and Evolutionary Computation. He has more than 35 research articles in reputed International journals and conferences, and 5 book chapters. He is currently working as a Professor at KPRIT, Hyderabad, India.
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