Time Series for H2O with Modeltime
Modeltime ecosystem was created to help with higher frequent time series, at scale, that's automated
I share some notes from an online community meetup on doing Time Series in H2O-3 with the Modeltime R package. The new R Package is neat, I hope that someone builds something like that for Python!
Notes from the Video
Matt Dancho, founder of Business Science
Introduced to H2O-3 via the AutoML package
Sample code in R shared
Sample forecasting project / Walmart Sales
Tidymodels standardize machine learning packages
Modeltime loads H2O
Multiple time series
Create a forecast time horizon, assess 52 weeks forecast
Create preprocessing steps, helps the H2O algos to find good features
Some columns are normalized from the pre-processing
Extracted Time-related features (i.e. week number, day of the week, etc)
Initializes H2O-3 / Stacked Ensemble model will be the best but hard to interpret
Modeltime workflow starts with a table
Modeltime is an organizational tool
Modeltime Calibrate will extract the residuals of the models
Visualize the forecast on the test set generates nice charts
Built a single H2O-3 model to predict 7 different time series
This is very scalable, instead of looping through everything
Refit the model on the entire training data and then did a forward walk of 52 weeks
Modeltime ecosystem was created to help with higher frequent time series, at scale, that's automated