Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Abstract

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss. The proposed model uses a real time dataset of fifteen Stocks as input into the system and based on the data, predicts or forecast future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting is done for the next quarter. Our model uses 3 main concepts for forecasting results. The first one is for stocks that show periodic change throughout the season, the ‘Holt-Winters Triple Exponential Smoothing’. 3 basic things taken into conclusion by this algorithm are Base Level, Trend Level and Seasoning Factor. The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm. The second concept is ‘Recurrent Neural Network’. The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network, the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop. The third concept is Recommendation System which filters and predict the rating based on the different factors.