The Datapred blog

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

The coal-gas spread and the price of emission allowances

Datapred starts coverage of European emission allowances

A procurement digital twin in the time of rising raw material prices

A digital twin for raw material procurement?

What is driving current European CO2 prices?

The 12 time windows of Procurement

Datapred announces the move of its Paris office to Maison RaiseLab

8 ways machine learning can boost your buying process

Staying ahead of procurement change

How raw material buyers can contribute to emission reduction

5 findings about contextual data and raw material price analysis

Covid-19 and raw materials - The many shapes of instability

Datapred raises Series A from JOIN Capital

Prediction and prescription - Continuous intelligence twins (Part 2/2)

Prediction and prescription - Continuous intelligence twins (Part 1/2)

The next big thing (in business intelligence)

Stop stacking, start aggregating

Datapred wins Airbus’s 2019 time series modeling challenge

Datapred named a Cool Vendor in Sourcing & Procurement Applications

Direct material procurement: beating the market is not the point

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

Productizing machine learning models - What is required?

How should you handle seasonality?

Advanced cross validation tips for time series

Best practices for bulletproof time series modeling

A better Facebook Prophet

Random Forest for predictive maintenance: Try harder

Machine learning for demand prediction: what works and what doesn’t

The basics of backtesting

The problem with deep learning and time series

What is time series, and why is it special?

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