Are Transformers Effective for Time Series Forecasting? · HackerPapers discussions

Are Transformers Effective for Time Series Forecasting? — mentioned by D-Machine

Tue, 23 Dec 2025 15:27:56 GMT

Just do some basic searches on e.g. Google Scholar for your task (e.g. "medical image segmentation", "point cloud segmentation", "graph neural networks", "timeseries classification", "forecasting") or task modification (e.g. "'rotation invariant' architecture") or whatever, sort by year, make sure to click on papers that have a large number of citations, and start reading. You will start to get a feel for domains or specific areas where transformers are and are not clearly the best models. Or just ask e.g. ChatGPT Thinking with search enabled about these kinds of things (and then verify the…

Paper and discussion history. A mention is not an endorsement.

Are Transformers Effective for Time Series Forecasting? — mentioned by cye131

Tue, 17 Jun 2025 22:53:55 GMT

I'm not a fan of this blog post as it tries to pass off a method that's not accepted as a good or standard time series methodology (graph transformers) as though it were a norm. Transformers perform poorly on time series, and graph deep learning performs poorly for tasks that don't have real behaviorial/physical edges (physical space/molecules/social graphs etc), so it's unclear why combining them would produce anything useful for "business applications" of time series like sales forecasting. For those interested in transformers with time series, I recommend reading this paper…

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Are Transformers Effective for Time Series Forecasting? — mentioned by dongobread

Tue, 16 Jul 2024 21:39:14 GMT

I think what you say is true when comparing transformers to CNNs/RNNs, but not to MLPs. Transformers, RNNs, and CNNs are all techniques to reduce parameter count compared to a pure-MLP model. If you took a transformer model and replaced each self-attention layer with a linear layer+activation function, you'd have a pure MLP model that can model every relationship the transformer does, but can model more possible relationships as well (but at the cost of tons more parameters). MLPs are more powerful/scalable but transformers are more efficient. Compared to MLPs, transformers save on parameter…

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Are Transformers Effective for Time Series Forecasting? — mentioned by ipsum2

Sat, 03 Feb 2024 07:45:55 GMT

This isn't for tabular data though? Time series transformers have been around for a few years now, see Transformers in Time Series: A Survey https://arxiv.org/abs/2202.07125 and Are Transformers Effective for Time Series Forecasting? https://arxiv.org/abs/2205.13504

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Are Transformers Effective for Time Series Forecasting? — HN submission

Mon, 29 Jan 2024 13:13:44 GMT

Are Transformers Effective for Time Series Forecasting?

3 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Are Transformers Effective for Time Series Forecasting? — mentioned by spyder

Mon, 10 Apr 2023 03:12:15 GMT

Yea, this paper says that the embarrassingly simple linear is better than transformers for time-series: https://arxiv.org/abs/2205.13504 from the abstract: "Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the…

Paper and discussion history. A mention is not an endorsement.