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Forecasting Models with Python

Last Update: June, 2018

1. Course Objective

Learn Forecasting Models main topics using Python programming language® 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).
  • Estimate non-seasonal and seasonal Auto Regressive Integrated Moving Average ARIMA models (Box-Jenkins).
  • 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 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.
My online courses are closed for enrollment.