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