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Among all the application areas of the time-series prediction, stock market prediction is the most challenging task due to its dynamic nature, and dependency on many volatile factors. The unpredictable fatal events called Black Swan events also highly influence the stock market. If the successful stock trends prediction is achieved, then the investors can adopt a more appropriate trading strategy, and that can significantly reduce the risk of investment. In this work, a time-efficient hybrid stock trends prediction framework(HSTPF) is proposed to successfully predict the future trends of the stock market even during the periods of Black Swan events. Here, to improve the prediction accuracy of HSTPF, the Black Swan events analysis and features selection operations are performed, and also the performance of various machine learning classifiers are analyzed. A vast number of experiments are conducted on the two real-world stock market datasets S&P BSE SENSEX and Nifty 50, to analyze the performance of the proposed framework. The framework is applied for the single-step and multi-step ahead predictions. The experimental results show that the proposed framework produces over 86% of accuracy, and during the Black Swan events, its accuracy is almost 80% for single-step ahead predictions. For the multi-step ahead of predictions, the HSTPF is produced satisfactory results. The framework also outperforms other existing similar works even during the Black Swan events in terms of prediction accuracy, and its computational time is also very low.

Recently, the stock market prediction has become one of the essential application areas of time-series forecasting research. The successful prediction of the stock market can be better guided to the investors to maximize their profit and to minimize the risk of investment. The stock market data are very much complex, non-linear and dynamic. Due to this reason, still, it is a challenging task. In recent time, deep learning method has become one of the most popular machine learning methods for time-series forecasting due to their temporal feature extraction capabilities. In this paper, we have proposed a novel Deep Learning-based Integrated Stacked Model (DISM) that integrates both the 1D Convolution neural network and LSTM recurrent neural network to find the spatial and temporal features from the stock market data. Our proposed DISM is applied to forecast the stock market. Here, we have also compared our proposed DISM with the single structured stacked LSTM, and 1D Convolution neural network models, and some other statistical models. We have observed that our proposed DISM produces better results in terms of accuracy and stability.

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Air quality (mainly PM2.5) forecasting plays an important role in the early detection and control of air pollution. In recent times, numerous deep learning-based models have been proposed to forecast air quality more accurately. The success of these deep learning models heavily depends on the two key factors viz. proper representation of the input data and preservation of temporal order of the input data during the feature’s extraction phase. Here we propose a hybrid deep neural network (HDNN) framework to forecast the PM2.5 by integrating two popular deep learning architectures, viz. Convolutional neural network (CNN) and bidirectional long short-term memory (BDLSTM) network. Here we build a 3D input tensor so that CNN can extract the trends and spatial features more accurately within the input window. Here we also introduce a linking layer between CNN and BDLSTM to maintain the temporal ordering of feature vectors. In the end, our proposed HDNN framework is compared with the state-of-the-art models, and we show that HDNN outruns other models in terms of prediction accuracy.