Forecasting Models with Python

Learn at your own pace with over 34 lectures and more than 5.5 hours of content.

At the end of this course you will be able to:

  • Approximate simple benchmarking methods.
  • Recognize time series patterns with moving averages and exponential smoothing methods.
  • Choose best methods comparing several forecasting errors’ metrics.
  • Appraise if time series is first order stationary or constant in its mean.
  • Estimate conditional mean with autoregressive integrated moving average (ARIMA) models.
  • Define models’ parameters and evaluate if forecasting errors are random.
  • Select best models comparing forecasting errors’ information criteria.
  • Check models’ forecasting accuracy comparing their predicting capabilities.

This course is best for you as:

  • Student at any knowledge level who wants to learn the subject.
  • Academic researcher who wishes to deepen your knowledge in data science, applied statistics, operations research, economics, econometrics or quantitative finance areas.
  • Business or financial analysts and data scientist who desires to apply these concepts in sales and financial forecasting, inventory optimization, demand and operations planning, or cash flow management.

The requirements for this course are:

  • Python programming language is required. Downloading instructions included.
  • Python Distribution and Integrated Development Environment are recommended. Downloading instructions included.
  • Python code files provided with course.
  • Familiarity with programming language is recommended.

Feel free to take a look at course curriculum here!