Time Series for H2O with Modeltime
H2Oai
AI Machine Learning
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 organiational 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 on 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
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