EXCERPT (BLOG ITEM + OG, TWITTER)
In this tutorial, we demonstrate a time series application of getML. We predict the hourly traffic volume on I-94 westbound from Minneapolis-St Paul. We benchmark our results against Facebook's Prophet. getML's relational learning algorithms outperform Prophet's classical time series approach by ~15%.
Summary:
Author: Sören Nikolaus
The dataset features some particularly interesting characteristics common for time series, which classical models may struggle to deal with appropriately. Such characteristics are:
The analysis is built on top of a dataset provided by the MN Department of Transportation, with some data preparation done by John Hogue.
In this notebbok, we compare getML's FastProp against well-known feature engineering libraries featuretools and tsfresh.
Summary:
Author: Sören Nikolaus
A common approach to feature engineering is to generate attribute-value representations from relational data by applying a fixed set of aggregations to columns of interest and perform a feature selection on the (possibly large) set of generated features afterwards. In academia, this approach is called propositionalization.
getML's FastProp is an implementation of this propositionalization approach that has been optimized for speed and memory efficiency. In this notebook, we want to demonstrate how – well – fast FastProp is. To this end, we will benchmark FastProp against the popular feature engineering libraries featuretools and tsfresh. Both of these libraries use propositionalization approaches for feature engineering.
In this notebook, we predict the hourly traffic volume on I-94 westbound from Minneapolis-St Paul. The analysis is built on top of a dataset provided by the MN Department of Transportation, with some data preparation done by John Hogue. For further details about the data set refer to the full notebook.
Initial Notebook:
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Propositionalization:
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