This repository contains code for performing time series analysis and training various models using TensorFlow and Keras. The project aims to provide a comprehensive framework for analyzing and modeling time series data, making it easier to develop accurate and robust predictive models.
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Data Loading and Visualization: The code includes functions to load two sets of time series data, time_series_1 and time_series_2, along with their corresponding target variable, y. It also provides visualization tools to help understand the characteristics and patterns present in the time series data.
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Data Preprocessing: Time series data often requires preprocessing steps to prepare it for model training. The code provides functions to handle common preprocessing tasks such as data combination, reshaping, and normalization. It ensures that the data is in the appropriate format for feeding into the models.
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Model Creation and Training: The repository offers a range of model architectures commonly used in time series analysis, including LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) models. These models are implemented using TensorFlow and Keras, providing flexibility and ease of use. The code includes functions to create, compile, and train the models using user-defined hyperparameters. Currently, the code is set up to use the Adam optimizer and sparse categorical cross-entropy loss, but these can be easily customized.
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Training Visualization: Monitoring the training process is crucial for model development. The code generates plots to visualize the training and validation loss during model training. These visualizations help assess the model's performance, detect overfitting, and determine the optimal number of training epochs. Early stopping techniques are also implemented to prevent overfitting and save computational resources.
By leveraging the power of TensorFlow and Keras, this repository provides a solid foundation for time series analysis and model training. Whether you are working on forecasting, anomaly detection, or other time series applications, this project can serve as a valuable resource to accelerate your development process.