Sample script related to RNN and LSTM model using many to many architecture
some more which can be done
For example, we can train a many-to-many LSTM model to predict the stock price movement of a particular company (e.g., Apple) based on a sequence of news articles related to the company. Each news article would be represented as a sequence of word embeddings, and the LSTM would learn to map these sequences to a sequence of binary outputs indicating whether the stock price went up or down on each day.
Here's a general outline of the steps involved in this process:
1.Collect a dataset of news articles and corresponding stock price data for the company of interest.
2.Preprocess the news articles by tokenizing them into words, removing stop words, and converting them to word embeddings using techniques such as word2vec or GloVe.
3.Preprocess the stock price data by computing daily price movements (e.g., the percentage change in the closing price from the previous day).
4.Split the data into training, validation, and testing sets.
5.Define and train a many-to-many LSTM model using the training data.
6.Evaluate the model on the validation and testing data.
7.Use the trained model to predict the stock price movement for new, unseen news articles.
8.Monitor the performance of the model over time and retrain it as necessary.
Of course, there are many variations and refinements that can be made to this basic approach. For example, one could incorporate other features such as technical indicators or social media sentiment scores, or use more sophisticated architectures such as attention-based models or transformer networks. Additionally, it's important to carefully consider the limitations and biases of the data and model, and to validate the predictions using real-world experiments.
LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN). In fact, LSTM is a specialized RNN architecture that is designed to better handle the vanishing gradient problem that occurs in traditional RNNs. So, LSTM is a variant of RNN that is widely used in various applications, including time-series forecasting and natural language processing.
several other architecture also available which also can try but i use M TO M because for the simulation purpose i want same features as a output which i have feed to the rnn as a input