Focuses on Time Series Forecasting Models in Python.
Moving Average(MA) Models Autoregressive (AR) Models Autoregressive Integrated Moving Average (ARIMA) Models Stationarity and its tests Forecasting with Seasonal Variations, Business Cycle Variations, Trends, Volatility etc. Differencing Correlation and Autocorrelation Functions(ACF) Impulse Response Functions(IRF) Augmented Dickey-Fuller Test(ADF) Granger Test of Causality Ljung-Box Test of Normal Distribution Application of the Bayesian Information Criterion Application of the Akaike Information Criterion Multivariate Vector Autoregressive (VAR) Models Cointegeration and Cointegration Tests Forecast and Forecast plots.