This script employs LSTM Symplectic Neural Networks to predict future numerical sequences. It loads, preprocesses & splits historical data for training & validation. Model is trained & visualized, then utilized to forecast future sequences & confidence rating. Adaptable to diverse datasets & prediction needs, offers customizable parameters for optimal performance

LSTM Sequence Prediction for Numerical Sequences The script utilizes a Long Short-Term Memory (LSTM) neural network to predict future sequences of numerical values. It trains on historical data and forecasts future sequences based on the learned patterns.

Overview

The script consists of the following main steps:

Data Loading and Preprocessing: Loads historical sequences of numbers from a text file and preprocesses the data for LSTM training.

Data Preparation: Splits the dataset into training and validation sets, and creates input-output pairs suitable for LSTM training.

LSTM Model Architecture: Defines the architecture of the LSTM neural network, including input shape, layer configurations, and output layer.

Model Training: Compiles and trains the LSTM model on the training data, monitoring the training and validation loss.

Prediction and Forecasting: Uses the trained model to predict future sequences of numbers, visualizing the predicted sequences over time.

Requirements

Python 3.x Libraries: numpy pandas scikit-learn tensorflow matplotlib Usage Ensure that you have Python installed on your system.

Install the required libraries using pip:

pip install numpy pandas scikit-learn tensorflow matplotlib

Place your historical data containing sequences of numbers in a text file. Update the file path in the script accordingly. Run the script. It will train the LSTM model and generate predictions for future sequences. Review the predicted sequences and confidence ratings.

Parameters time_steps: Number of time steps used for creating input-output pairs. future_periods: Number of future periods to forecast. start_date: Start date for forecasting future sequences.

Outputs Training and validation loss curves plotted over epochs. Predicted sequences of numbers with corresponding confidence ratings. Visualization of predicted sequences over time.

Notes Adjust the model architecture and hyperparameters as needed for better performance. Ensure an adequate validation set size for reliable prediction. This script assumes each line in the text file contains a single number as a string.