Welcome to my Time Series Analysis repository! 🚀
This repository is a collection of Jupyter notebooks where I explore and implement various time series analysis concepts. It is not a full-fledged project but rather a learning space where I experiment with different techniques and datasets to deepen my understanding of time series forecasting, decomposition, and modeling.
- 📁 Project_name/ – Contains Jupyter notebooks covering different aspects of time series analysis.
- 📄 README.md – This document, explaining the repository's purpose and structure.
- 📄 requirements.txt – List of dependencies for setting up the environment.
In these notebooks, I explore a variety of time series techniques, including but not limited to (Some concepts listed are TBD):
- ✅ Time Series Decomposition (Trend, Seasonality, Residuals)
- ✅ Stationarity & Differencing (Dickey-Fuller Test, ACF/PACF)
- ✅ Smoothing Techniques (Moving Averages, Exponential Smoothing)
- ✅ Classical Forecasting Models (AR, MA, ARMA, ARIMA, SARIMA)
- ✅ Machine Learning for Time Series (Random Forest, XGBoost, etc.)
- ✅ Deep Learning for Time Series (LSTMs, CNNs, Transformers)
- ✅ Anomaly Detection (Outlier detection in time series)
Each notebook contains explanations, code implementations, and insights gained from different datasets.
I experiment with different time series datasets, including:
- Stock market data
- Ice Cream Production Data
Datasets may either be sourced from public repositories (Kaggle, UCI, etc.) or self-generated.
To run the notebooks locally, follow these steps:
# Clone the repository
git clone https://github.com/bhanurana430/time-series.git
cd time-series-analysis
# Install dependencies
pip install -r requirements.txt
This is a personal learning repository, but if you have suggestions, feel free to open an issue or a pull request!
I'm always open to discussions and collaborations. 🚀