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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.