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TimeGPT-1 | Hacker News

news.ycombinator.com · 5,430 words · saved by 1 readers

On extremely high dimensional data (I worked at a credit card processor company doing fraud modeling), deep learning dominates, but there's simply no advantage in using a designated "time series" model that treats time differently than any other feature. We've tried most time series deep learning models that claim to be SoTA - N-BEATS, N-HiTS, every RNN variant that was popular pre-transformers, and they don't beat an MLP that just uses lagged values as features. I've talked to several others in the forecasting space and they've found the same result. On mid-dimensional data, LightGBM/Xgboost is by far the best and generally performs at or better than any deep learning model, while requiring much less finetuning and a tiny fraction of the computation time. And on low-dimensional data, (V)ARIMA/ETS/Factor models are still king, since without adequate data, the model needs to be structured with human intuition. As a result I'm extremely skeptical of any of these claims about a generally

As someone who's worked in time series forecasting for a while, I haven't yet found a use case for these "time series" focused deep learning models. On extremely high dimensional data (I worked at a credit card processor company doing fraud modeling), deep learning dominates, but there's simply no advantage in using a designated "time series" model that treats time differently than any other feature. We've tried most time series deep learning models that claim to be SoTA - N-BEATS, N-HiTS, every RNN variant that was popular pre-transformers, and they don't beat an MLP that just uses lagged…

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