In this paper, we propose an approach towards predicting the trend of stock price values by analyzing the relevant words occurring in social media like Twitter and by performing a time series analysis of the performance of the stock over the years. We obtain training data and train them separately against normalized values of stock prices themselves using neural networks and obtain the desired results by using the outputs of these separate approaches as the training data for another separate neural network that predicts the trend in the stock's future pricing along with the values with a certain degree of accuracy.

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Summary The present existing weather prediction systems deployed by weather forecasting agencies predict the anomalous events using numerical simulation models developed based on geophysics and oceanographic theories. The major disadvantage with existing models is that are explicit simulation models. So, we try to build a real time prediction system that will automatically predict the intensity of any marine natural disaster events and alert the authorities to take necessary actions using a satellite and observational real time data set including moored met ocean data. We propose a novel hybrid model which is a combination of Convolutional Neural Network (CNN) and Long Short‐Term Memory (LSTM). The paper analyzes various other models such as Gated Recurrent Unit (GRU), Bidirectional LSTM (Bi‐LSTM) Recurrent Neural Network (RNN), and Extreme Learning Machine. In addition the other standard CNN architectures are also analyzed. Since the residual CNN network is the most effective architecture, the paper proposes a modification of the residual CNN network by combining the residual CNN network with LSTM/GRU RNN network along with a proposed new activation function which was experimentally proved to be more effective than the deep learning community standard activation function ReLU. The proposed new activation function overcomes the dead neuron propagation problem in ReLU activation function and hence the model's accuracy is increased by more than 1%. The proposed CNN + LSTM/GRU model is benchmarked with UCR time series benchmark dataset and it is experimentally seen that the model performs better than all existing algorithms.