Last Update: April, 2018
1. Course Objective
Learn Forecasting Models main topics using R statistical software® in this practical course for all knowledge levels. Feel free to take a look at Course Curriculum.
2. Skills Learned
At the end of this course you will know how to:
- Estimate simple forecasting methods (arithmetic mean, random walk, seasonal random walk, random walk with drift).
- Approximate simple moving averages and exponential smoothing methods (Brown simple exponential smoothing, Holt linear trend, Gardner additive damped trend, Taylor multiplicative damped trend and Holt-Winters seasonal methods).
- Assess if time series is first order trend stationary (augmented Dickey-Fuller unit root test, Phillips-Perron unit root test).
- Estimate non-seasonal and seasonal Auto Regressive Integrated Moving Average ARIMA models (Box-Jenkins).
- Select methods or models (Akaike, corrected Akaike and Schwarz Bayesian information loss criteria).
- Evaluate methods or models forecasting accuracy (mean absolute error, root mean squared error, mean absolute percentage error, mean absolute scaled error).
- Assess model residuals white noise requirement (Ljung-Box autocorrelation 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 data science, applied statistics, operations research, economics, econometrics or quantitative finance.
- Business data scientist who desires to apply this knowledge in sales and financial forecasting, inventory optimization, demand and operations planning or cash flow management.