Combining PyCaret and TimeMachines for Time-Series Prediction

The popular open-source PyCaret package provides automated machine learning capability, allowing the user to search hundreds of regression models. The TimeMachines package provides a variety of incremental (online) time-series algorithms. In this short post, we cover the nuts and bolts of using these two libraries together. We shall

1. Grab a live time series from microprediction
2. Fit with pycaret
3. Run some timemachines models
4. Fit with pycaret again

All code is provided in https://github.com/microprediction/timeseries-notebooks/blob/main/pycaret_microprediction_timemachines.ipynb. Let’s get a few preliminaries out of the way.

!pip install pycaret[full]
!pip install --upgrade statsmodels
!pip install microprediction
!pip install timemachines
import microprediction
from datetime import datetime, timedelta
from microprediction import MicroReader
import random
import matplotlib.pyplot as plt
import pandas as pd

If you get stuck here, I recommend

!pip install --upgrade pip

Retrieving live time-series data from microprediction.org