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.
0:00 – Introduction
1:12 – ACF and PACAF plots
2:50 – Building the ARIMA model
5:53 – Forecasting
8:27 – Comparison
Watch Part 1 Here:
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Watch Part 3 Here:
https://tutorials.datasciencedojo.com...
Code, R & Python Script Repository:
https://code.datasciencedojo.com/rebe...
Packages Used:
pandas
matplotlib
StatsModels
statistics
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