Last Update: June, 2018
1. Course Objective
Learn Advanced Forecasting Models main topics using Python programming language® in this practical course for expert knowledge level. Feel free to take a look at Course Curriculum.
2. Skills Learned
At the end of this course you will know how to:
- Identify Box-Jenkins Non-Seasonal and Seasonal Auto Regressive Integrated Moving Average ARIMA and SARIMA models integration order (first order trend stationary augmented Dickey-Fuller unit root test), seasonal integration order (first order seasonal stationary deterministic test), non-seasonal, seasonal autoregressive and moving average orders (normal and partial autocorrelation functions charts).
- Estimate ARIMA and SARIMA models (non-seasonal, seasonal random walk with drift and differentiated first order autoregressive).
- Recognize Generalized Auto Regressive Conditional Heteroscedasticity GARCH modeling need (second order stationary Engle ARCH test) and non-Gaussian GARCH modeling need (multiple order stationary Jarque-Bera normality test).
- Approximate ARIMA models with residuals assumed as Gaussian or Student’s t distributed and with Bollerslev simple, Nelson exponential or Glosten-Jagannathan-Runkle threshold GARCH effects (random walk with drift, differentiated first order autoregressive).
- Select methods or models (Akaike and Schwarz Bayesian information loss criteria).
- Evaluate methods or models forecasting accuracy (mean absolute error, root mean squared error).
- Assess model standardized residuals strong white noise requirement (Ljung-Box autocorrelation test, Engle ARCH test, Jarque-Bera normality test).
3. Typical Student
This course is ideal for you as:
- Undergraduate or postgraduate who wants to learn about the subject.
- Academic researcher who wishes to deepen your knowledge in advanced applied statistics, econometrics or quantitative finance.
- Experienced finance professional or business data scientist who desires to apply this knowledge in advanced investment management research or sales forecasting.