Treffer: Enhancing Stock Market Forecasting by Integrating Traditional Time Series Models with Technical Indicators: A Data Analytics Approach.

Predicting stock market movements is always a challenging task due to how unpredictable and constantly changing the market can be. This study aims to improve the accuracy of stock price forecasts by combining traditional time series models with technical indicators. Time series models like ARIMA and Exponential Smoothing are great for spotting patterns and trends in historical data, but they often miss short-term changes. In contrast, technical indicators such as the Relative Strength Index (RSI), MACD, and Bollinger Bands help identify market momentum and shifts in investor behavior. To build a more reliable prediction model, this research uses a combination of these tools. We analyzed historical stock price data from the National Stock Exchange (NSE) of India, covering the years 2016 to 2024. The focus was on major companies from sectors like banking, IT, and FMCG to ensure that the approach works across different industries. Python was used for data cleaning, analysis, and modeling, using libraries like pandas, statsmodels, and ta. The results show that when technical indicators are added to time series models, the predictions become more accurate. This hybrid method works especially well during market ups and downs, making it a strong and flexible forecasting tool. Overall, the study offers a practical and data-driven solution for investors and analysts who want to make better-informed decisions in the stock market. It also helps bridge the gap between traditional statistical methods and real-time market behavior. [ABSTRACT FROM AUTHOR]

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