Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Prakasan, Adwaidh; Harimoorthy, Karthikeyan; Ganesh Kumar, R. Title A name given to the resource Enhancing Stock Price Prediction: A Multi-Model Framework Integrating Technical Indicators and Sentiment Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Conference on Innovative Practices in Technology and Management, ICIPTM 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICIPTM69057.2026.11465544" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICIPTM69057.2026.11465544</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037857949?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037857949?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Prakasan A., Christ University, Department of Computer Science and Engineering, Bangalore, India; Harimoorthy K., Christ University, Department of Computer Science and Engineering, Bangalore, India; Ganesh Kumar R., Christ University, Department of Computer Science and Engineering, Bangalore, India Description An account of the resource This paper proposes a multi-model strategy that would improve the predictive power of stock prices by combining time-series analytics with external market indicators. The system allows five different base prediction methods; Long Short-Term Memory (LSTM), Enhanced Bidirectional LSTM (XLSTM), Support Vector Machine (SVM) which may use radial basis function (rbf), linear or polynomial (poly) kernels, Autoregressive integrated moving average (ARIMA), and Seasonal Autoregressive integrated moving average (SARIMA). A stacking procedure which uses linear regression as a meta-model together with a voting ensemble method is then employed to link these base models. The feature engineering is thorough, as it provides for general price and volume data, a battery of technical indicators (SMA10, SMA20, EMA 12, EMA 26, MACD elements, and RSI14) and a general sentiment indicator (summarised financial news). Sentiment analysis is performed by a pipeline that is trained using RoBERTa and yields discrete numerical values (0 negative, 1 neutral, 2 positive). The model's capability is very rigorously gauged by the conventional metrics Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy (DA). The real-world results demonstrate that the ensemble method is very efficient where the stacking arrangement leads to the lowest total MAPE of 0.6027 % MSFT and the highest directional Accuracy of 75.86 % GOOGL, thus, providing a strong evidence for the effectiveness of the thorough integration of heterogeneous machine-learning, statistical, and sentiment- analysis methods to produce the most accurate financial forecasts. 2026 IEEE. Subject The topic of the resource ARIMA; Ensemble Learning; Financial Forecasting; LSTM; Sentiment Analysis; Stock Price Prediction; Support Vector Machine (SVM); Technical Indicators; Time Series Analysis; XLSTM Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-831954328-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online