The Datapred blog

On machine learning, time series and how to use them.

Custom loss functions - What they are and why you need them

Machine learning models often disappoint in production for a simple reason - they have the wrong target.
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How should you handle seasonality?

on Jan 14, 2019 2:07:43 PM By | Datapred | 0 Comments | predictive analysis seasonality holt-winters
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.  
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Advanced cross validation tips for time series

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.
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Best practices for bulletproof time series modeling

on Oct 30, 2018 9:03:51 AM By | Datapred | 0 Comments | time series machine learning backtesting backtest predictive analysis modeling
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.  
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A better Facebook Prophet

on Oct 9, 2018 2:30:17 PM By | Datapred | 0 Comments | machine learning demand prediction Prophet predictive analysis facebook
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.
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