Improving Stock Market Forecasting Using Advanced Time Series Analysis Techniques

The mechanism of a stock exchange is very dynamic and intricate, making it difficult to forecast its conduct. A typical method for analyzing historical stock market data and predicting future trends is time series data analysis. With this strategy, past stock market data is examined for patterns, trends, and correlations to spot prospective market shifts in the future. Moving averages, autoregressive models, and exponential smoothing are a few statistical methods used in Improving Stock Market Forecasting using Advanced Time Series Analysis Techniques to find periodic and unusual changes in data. These techniques may be used to provide projections based on past data, empowering traders, and investors to choose when and how to purchase and sell stocks with more knowledge. Because of the erratic and unpredictable nature of the market, forecasting the stock market remains a difficult subject despite advances in time series analytic tools. This research intends to improve the precision of the Holt-Winters model and Simple moving average by using so-phisticated forecasting methods and optimisation algorithms via a thorough investigation of Improving Stock Market Forecasting using Advanced Time Series Analysis prediction which gives a more efficient result