This project implements a deep learning approach to analyze and predict patterns in time series data. Using convolutional neural networks (CNNs), we aim to capture temporal dependencies and extract meaningful features from sequential data patterns.
Time series analysis presents unique challenges in data science, from handling seasonal variations to capturing long-term dependencies. Traditional statistical methods often struggle with complex, non-linear patterns in modern time series data. This project explores how deep learning can help overcome these limitations.
- Develop a robust model for time series pattern recognition
- Evaluate model performance across different types of time series data
- Create a scalable architecture that can handle varying input sizes
- Provide insights into temporal patterns and dependencies
- Deep learning based time series analysis
- Flexible architecture to handle multiple input formats
- Built-in performance monitoring and evaluation
- Efficient data preprocessing pipeline
torch
numpy
pandas