on Jun 24, 2019 3:27:36 PM By |
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Data scientists are waking up to the fact that combining multiple, relatively simple predictive models is often more efficient, when tackling a time series forecasting challenge, than using the latest super-duper neural network.
on May 9, 2019 11:32:00 AM By |
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Datapred's machine learning software for direct material procurement helps companies save 3-5% off their commodity, energy and raw material costs year over year, through a unique combination of price predictions and constraint optimization.
on Mar 19, 2019 12:37:43 PM By |
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We talk a lot with the data science teams of industrial companies, and it is striking how unaware they usually are of the gap between a good machine learning model and a production-ready machine learning application.
on Jan 14, 2019 2:07:43 PM By |
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Seasonality - recurring but not necessarily periodic data patterns - is a staple of time series modeling. Since capturing true seasonality greatly enhances model accuracy, we wanted to share our thoughts and experience on the detection and modeling of such data patterns.
on Dec 7, 2018 11:43:38 AM By |
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In a previous post, we explained the concept of cross-validation for time series, aka backtesting, and why proper backtests matter for time series modeling.
on Oct 30, 2018 9:03:51 AM By |
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Our goal in this post is to discuss our standard strategy (beyond respecting basic time series modeling principles) for building accurate predictive models. We will use the example of commodity procurement optimization.
on Oct 9, 2018 2:30:17 PM By |
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What is Facebook's Prophet? Prophet is a forecasting (i.e. time-series specific) algorithm open-sourced by Facebook in February 2017, and belonging to the GAM family of algorithms.
