• Manoj Lipton
• Dr.Sarvottam Dixit
• Dr. Asif Ullah Khan

Time Series Prediction is a time series analysis field designed to develop models that can predict future values for a series of time-ordered values (time series). Because the price of a stock can be considered a historical series because its values change over time, time series forecasting techniques can also be used to predict stock prices. This research work investigates stock market indices prediction that is an interesting and important research in the areas of investment and applications, as it can get more profits and returns at lower risk rate with effective exchange strategies. To realize accurate prediction, various methods have been tried, among which the machine learning methods have drawn attention and been developed. In this research work, a soft computing or machine learning approach is used to design an algorithm which is a basic hybridized framework of the feature reduced adaptive resonance theory (ART) and support vector machine (SVM) to effectively predict stock market indices from the historical dataset. Ten different technical indicators are extracted.

Machine learning, ART, SVM, Stock Market Indices, Technical indicators, Stock Prediction.

"Design and Development of Model using Soft Computing for Stock Market Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.559-577, December-2018, Available :http://www.jetir.org/papers/JETIR1812382.pdf

"Design and Development of Model using Soft Computing for Stock Market Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp559-577, December-2018, Available at : http://www.jetir.org/papers/JETIR1812382.pdf