• CH MANIPRASAD
• O.MRUDHULA
• Dr.A.M.SOWJANYA

Accurate prediction of Stock Market data is a major research problem in Timeseries community. Anything that is observed sequentially over time is a timeseries. Since Stock Market can make huge impact on an investor’s life, so before taking a decision we need to analyze all the factors of stock data. In this paper we proposed an ensemble technique to improve prediction of stock market trends. Exponential smoothing is one of the most popular forecasting methods. Here we use a technique for bootstrap aggregation (bagging) of exponential smoothing methods, which results in significantly improved forecasts. Bagging uses a Box-Cox transformation followed by STL decomposition to separate the timeseries into trend, seasonal part, and remainder.

Timeseries, forecasting, ensemble, bagging, bootstrapping, exponential smoothing, BaggedETS

"PREDICTION OF STOCK MARKET TRENDS USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.302-308, December-2018, Available :http://www.jetir.org/papers/JETIR1812244.pdf

"PREDICTION OF STOCK MARKET TRENDS USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp302-308, December-2018, Available at : http://www.jetir.org/papers/JETIR1812244.pdf