TIME SERIES MODELLING USING EXCEL, R & PYTHON

Description

Section 1 – Introduction to time series data and components of time series models
Section 2 – Estimate simple forecasting methods such as arithmetic mean, random walk, seasonal random walk and random walk with drift
Section 3 – Approximate simple moving averages and exponential smoothing methods with no trend or seasonal patterns such as Brown simple exponential smoothing method.
Section 4 – Approximate exponential smoothing methods with trend and seasonal patterns such as Holt-Winters additive, Holt-Winters multiplicative and Holt-Winters damped methods
Section 5 – Stationary Series & Unit Root test
Section 6 – Importance of differencing
Section 7 – Auto correlation (ACF) and partial auto correlation functions (PACF)
Section 6 – Box Jenkins methods (ARIMA models)/li>
Section 7 – Model diagnostics and residual analysis
Section 8 – Models Forecasting Accuracy
Section 9 – Multivariate Time Series Modelling
Section 10 – Cointegrated Time Series Models
Section 11 – Time Varying Volatility & GARCH/ARCH Models
Section 12 – Recurrent Neural Networks
Section 13 – Long Short Time Memory Neural Networks