Introduction I’ve spoken about Jeremy Howard many times before, but of all the things that he has opened my eyes to Kaggle, the popular data science competition platform, is the one thing that unti...

In my last post we took a dive into the 2022 paper, Are Transformers Effective for Time Series Forecasting and looked at how the DLinear and NLinear models that it introduces perform in both multiv...

You know in a world where there are more transformers in machine learning research than there are in my son’s bedroom, it was a breath of fresh air when I read the 2022 paper Are Transformers Effe...

I’ve written about GluonTs before and it’s a great library for time series forecasting, however did you know that most of it’s models are built on top of MXNet and for some architectures there is a...

With the British Grand Prix just around the corner it’s a GO, GO, GO as we launch our Formula 1 themed Time Series Model Championships. Yes, that’s right folks, us at the totally not made up Federa...

When did you first hear the term GenAI or GenerativeAI? A year ago or 2 years maybe. Now check out this video generated with the help from an AI model called DeepDream by Google. DeepDream Groce...

1. Introduction In this, a follow up to my two previous posts DeepAR and superconvergence, I will show you how to obtain state of the art performance using my simplified version of the DeepAR mode...

1. Introduction Want to know how to get a performance boost from your neural network for virtually no extra effort? Well I’m going to show you how to do it with a learning rate scheduling system c...

1. Introduction Hey Amigos! Four years since its release, DeepAR is an old timer in the world of neural networks for time series forecasting, but it can still outshoot many of the young guns out t...

1. Introduction This blog accompanies a paper I wrote on the effect of covariates on LSTM networks for timeseries forecasting, which you can find on arxiv. In business, leading indicators serve a...