Detailed implementation of various time series analysis models and concepts on real datasets.
- White Noise Model
- Random Walk
- AR Model
- ARIMA Model
- Vector Autoregressive Regression (VAR) Model
- Volatility Modelling using ARCH & GARCH Models
- Markov Switching Dynamics Regression (MSDR) Model
You will find implementation of below concepts which can be used for your reference:
- Log Returns
- White Noise Model
- White Noise Tests - Autocorrelation plot and Ljung Box Test
- Random Walk
- Time Series Decomposition
- AR(p) Model
- MA(q) Model
- ARMA(p,q) Model
- ARIMA(p,q,d) Model
- Augumented Dickey Fuller Test - Check for Stationarity / Non-Stationarity
- Differencing Method
- Autocorrelation Function (ACF)
- Partial Autocorrelation Function (PACF)
- Model Selection Criterion - AIC, BIC, HQC
- Model Diagnostics
- Residual Diagnostics
- Normal Q-Q plot
- Forecasting return
- Multi-Variate Time Series Analysis
- VAR(p) Model
- Impulse Response Functions
- Volatility Modelling
- ARCH(p) model
- GARCH(p,q) model
- Jarque Bera Test
- Forecasting volatility - One step ahead and N step ahead
- Hidden Markov Models (HMM)
- Markov Switching Dynamic Regression (MSDR) Model