Mean Absolute Error for Forecast Evaluation: Time Series in Python Part 3

In part 3 of this video series, learn how to evaluate time series model predictions using mean absolute error and Python’s statistics and matplotlib packages. We look at plotting the differences between actual versus predicted values, and calculate the mean absolute error to help evaluate our ARIMA time series model. We also look at potential issues when modeling time series, and how to take this further and learn more in-depth. This series is considered for intermediate and advanced users. We have a data science bootcamp for complete beginners!

Watch Part 1:
Read and Transform your Data: Time Series in Python

Watch Part 2:
ARIMA modeling and forecasting: Time Series in Python

Code, R & Python Script Repository

Packages Used:

More Data Science Material:
[Video] Getting started with Python and R for Data Science
[Video] Web scraping in Python and Beautiful Soup
[Blog] Breaking the Curse of Dimensionality with Python


Rebecca Merrett
About The Author
- Rebecca holds a bachelor’s degree of information and media from the University of Technology Sydney and a post graduate diploma in mathematics and statistics from the University of Southern Queensland. She has a background in technical writing for games dev and has written for tech publications.

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