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.

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:
pandas
matplotlib
StatsModels
statistics

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

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

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