Stock Prediction and Forecasting Using LSTM(Long-Short-Term-Memory)

In an ever-evolving world of finance, accurately predicting stock market movements has long been an elusive goal for investors and traders alike. While countless strategies and models have emerged over the years, one approach has recently gained significant traction due to its ability to capture complex patterns and dependencies in historical data: Long Short-Term Memory (LSTM). Leveraging the power of deep learning, LSTM offers a promising avenue for unlocking insights into the unpredictable nature of the stock market. In this article, we delve into the realm of LSTM-based stock market predictions and explore how this innovative approach has the potential to transform investment strategies. <img alt="Plotted predictions after successful analysis and forecasting." src="https://miro.medium.com/v2/resize:fit:609/1*p3VtiP8F8f_PvoBJr6e_aA.png" style="height:451px; width:609px" /> Plotted predictions after successful analysis and forecasting. At its core, LSTM is a variant of Recurrent Neural Networks (RNNs), designed specifically to address the vanishing gradient problem that plagues traditional RNNs. The vanishing gradient problem refers to the phenomenon where the gradients of early layers in the network become increasingly small, hindering their ability to capture long-term dependencies. LSTM overcomes this limitation by incorporating memory cells, gates, and carefully engineered connections, enabling it to selectively retain and propagate information over extended time intervals. This unique architecture allows LSTM models to capture intricate temporal relationships in sequential data, making them particularly well-suited for predicting time series data, such as stock prices. <a href="https://medium.com/@prajjwalchauhan94017/stock-prediction-and-forecasting-using-lstm-long-short-term-memory-9ff56625de73">Website</a>