While AutoML is a powerful tool for automated machine learning, it's not widely used by most people. Personally, I wouldn't pay thousands of dollars for fancy hyperparameter optimization. In most cases improvements are marginal.

One of the cool features of Big Query is its seamless integration with SQL queries, which makes data analysis much easier.

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

The NHITS model enhances the multi-step forecasting strategy by incorporating innovative hierarchical interpolation and multi-rate data sampling techniques inspired by wavelet analysis.

It assembles its predictions sequentially and emphasizes its components with different frequencies and scales. NHITS significantly improves accuracy in long-horizon forecasting tasks while reducing computation time by orders of magnitude compared to existing neural forecasting approaches.

Convolutional Neural Networks are an excellent example of how correct inductive biases can:

1. Reduce number of parameters.
2. Improve performance.

There is long way to go for AutoML solutions. Thanks for confirming I was not the only one.

All the models are global models, trained using cross learning. Not single models per series. Unless the experiments were done like that.

NBEATs hyper-parameters are minimally explored in the original paper the ensemble was not tuned. There is something broken with the reported times.

The M3 dataset consists only of 3,003 series, a minimal improvement of DL is not a surprise. Everybody knows that neural networks require large datasets to show substantial improvements over statistical baselines.

What is truly surprising is the time it takes to train the networks, 13 days for thousand series

=> there must be something broken with the experiments