This repository contains code and materials related to the Time Series and Sequential Data Analysis lab, completed during the 3rd year of college.
In this lab, we explore various techniques and algorithms for analyzing time-series data and sequential data. Time-series analysis is crucial in many domains, including finance, weather forecasting, and signal processing, among others. Understanding sequential data is also essential in natural language processing, speech recognition, and many other applications.
- Data: This directory contains datasets used for experimentation and analysis.
- Notebooks: This directory contains Jupyter notebooks with code for various analysis tasks.
- Scripts: This directory contains Python scripts for specific tasks or analyses.
- Results: This directory contains the results of experiments, including plots, charts, and analysis reports.
- Time series decomposition
- Seasonal decomposition
- Autoregressive Integrated Moving Average (ARIMA) modeling
- Exponential Smoothing methods
- Long Short-Term Memory (LSTM) networks for sequential data analysis
- Recurrent Neural Networks (RNNs) for time-series prediction
- Evaluation metrics for time-series forecasting
- Python 3.x
- Jupyter Notebook
- NumPy
- pandas
- Matplotlib
- scikit-learn
- TensorFlow (for deep learning models)
- Clone this repository to your local machine.
- Install the required dependencies using
pip install -r requirements.txt
. - Explore the notebooks and scripts in the
Notebooks
andScripts
directories, respectively. - Run the code cells in the notebooks to see the analysis results.
- Experiment with different datasets and algorithms to deepen your understanding of time-series and sequential data analysis.
Contributions are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.
This project is licensed under the BSD-2 License - see the LICENSE file for details.