ARIMA modeling and forecasting: Time Series in Python Part 2

In part 2 of this video series, learn how to build an ARIMA time series model using Python’s statsmodels package and predict or forecast N timestamps ahead into the future. Now that we have differenced our data to make it more stationary, we need to determine the Autoregressive (AR) and Moving Average (MA) terms in our model. To determine this, we look at the Autocorrelation Function plot and Partial Autocorrelation Function plot. This series is considered for intermediate and advanced users.

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

Watch Part 3 Here:
Mean Absolute Error for Forecast Evaluation: 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] Supercharge your Python Plots with Zero Extra Code


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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