Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction

Designing robust and accurate prediction models has been a viable research area since a long time. While proponents of a well-functioning market predictors believe that it is difficult to accurately predict market prices but many scholars disagree. Robust and accurate prediction systems will not only be helpful to the businesses but also to the individuals in making their financial investments. This paper presents an LSTM model with two different input approaches for predicting the short-term stock prices of two Indian companies, Reliance Industries and Infosys Ltd. Ten years of historic data (2012-2021) is taken from the yahoo finance website to carry out analysis of proposed approaches. In the first approach, closing prices of two selected companies are directly applied on univariate LSTM model. For the approach second, technical indicators values are calculated from the closing prices and then collectively applied on Multivariate LSTM model. Short term market behaviour for upcoming days is evaluated. Experimental outcomes revel that approach one is useful to determine the future trend but multivariate LSTM model with technical indicators found to be useful in accurately predicting the future price behaviours.

Automatic Text Summarization Methods: A Comprehensive Review

One of the most pressing issues that have arisen due to the rapid growth of the Internet is known as information overloading. Simplifying the relevant information in the form of a summary will assist many people because the material on any topic is plentiful on the Internet. Manually summarising massive amounts of text is quite challenging for humans. So, it has increased the need for more complex and powerful summarizers. Researchers have been trying to improve approaches for creating summaries since the 1950s, such that the machine-generated summary matches the human-created summary. This study provides a detailed state-of-the-art analysis of text summarization concepts such as summarization approaches, techniques used, standard datasets, evaluation metrics and future scopes for research. The most commonly accepted approaches are extractive and abstractive, studied in detail in this work. Evaluating the summary and increasing the development of reusable resources and infrastructure aids in comparing and replicating findings, adding competition to improve the outcomes. Different evaluation methods of generated summaries are also discussed in this study. Finally, at the end of this study, several challenges and research opportunities related to text summarization research are mentioned that may be useful for potential researchers working in this area.