⏱️ Time Series Analysis 🧑‍💻

A collection of assessments in Time Series Analysis completed as part of my Econometrics program. I intend this particular repository to focus on Time Series Forecasting Models in Python. I have used Google's Cloud-based Colaboratory Jupyter IDE for the purposes of this repository.

Topics covered in this repository include but are not limited to:

1. Regression Models
2. Moving Average(MA) Models
3. Autoregressive (AR) Models
4. Autoregressive Integrated Moving Average (ARIMA) Models
5. Stationarity and its tests
6. Forecasting with Seasonal Variations, Business Cycle Variations, Trends, Volatility etc.
7. Differencing
8. Correlation and Autocorrelation Functions(ACF)
9. Impulse Response Functions(IRF)
10. Augmented Dickey-Fuller Test(ADF)
11. Granger Test of Causality
12. Ljung-Box Test of Normal Distribution
13. Application of the Bayesian Information Criterion
14. Application of the Akaike Information Criterion
15. Autoregressive Conditional Heteroskedastic (ARCH) Models
16. Generalised Autoregressive Conditional Heteroskedastic (GARCH) Models
17. Multivariate Vector Autoregressive (VAR) Models
18. Cointegeration and Cointegration Tests

Comments, suggestions always welcome.