Comparative Study of Predicting Stock Index Using Deep Learning Models

Time series forecasting has seen many methods attempted over the past few decades, including traditional technical analysis, algorithmic statistical models, and more recent machine learning and artificial intelligence approaches. Recently, neural networks have been incorporated into the forecasting scenario, such as the LSTM and conventional RNN approaches, which utilize short-term and long-term dependencies. This study evaluates traditional forecasting methods, such as ARIMA, SARIMA, and SARIMAX, and newer neural network approaches, such as DF-RNN, DSSM, and Deep AR, built using RNNs. The standard NIFTY-50 dataset from Kaggle is used to assess these models using metrics such as MSE, RMSE, MAPE, POCID, and Theil's U. Results show that Deep AR outperformed all other conventional deep learning and traditional approaches, with the lowest MAPE of 0.01 and RMSE of 189. Additionally, the performance of Deep AR and GRU did not degrade when the amount of training data was reduced, suggesting that these models may not require a large amount of data to achieve consistent and reliable performance. The study demonstrates that incorporating deep learning approaches in a forecasting scenario significantly outperforms conventional approaches and can handle complex datasets, with potential applications in various domains, such as weather predictions and other time series applications in a real-world scenario.

Prediction of IPL Match Outcome Using Machine Learning Techniques

India's most popular sport is cricket and is played across all over the nation in different formats like T20, ODI, and Test. The Indian Premier League (IPL) is a national cricket match where players are drawn from regional teams of India, National Team and also from international team. Many factors like live streaming, radio, TV broadcast made this league as popular among cricket fans. The prediction of the outcome of the IPL matches is very important for online traders and sponsors. We can predict the match between two teams based on various factors like team composition, batting and bowling averages of each player in the team, and the team's success in their previous matches, in addition to traditional factors such as toss, venue, and day-night, the probability of winning by batting first at a specified match venue against a specific team. In this paper, we have proposed a model for predicting outcome of the IPL matches using Machine learning Algorithms namely SVM, Random Forest Classifier (RFC), Logistic Regression and K-Nearest Neighbor. Experimental results showed that the Random Forest algorithm outperforms other algorithms with an accuracy of 88.10%.