Evaluating Our Model with statistics and matplotlib | Time Series in Python Part 3

In part 3 of this video series, learn how to evaluate time series model predictions using 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.

0:00 – Introduction
0:45 – Reading the sample
4:19 – Predicted values to the training set
5:15 – Plot actual versus predicted values
7:05 – Evaluating the model
12:22 – Testing the model

Watch Part 1:
https://tutorials.datasciencedojo.com/arima-model-time-series-python/

Watch Part 2:
https://tutorials.datasciencedojo.com/arima-model-time-series-python/

Code, R & Python Script Repository:
https://code.datasciencedojo.com/rebeccam/tutorials/tree/master/Time%20Series

Packages Used:
pandas
matplotlib
StatsModels
statistics
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