Zero-Shot Forecasting: Our Search for a Time-Series Foundation Model
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Re: Zero-Shot Forecasting: Our Search for a Time-Series Foundation Model
#2Interesting, what are the usecases youre using the models for? Would like to know more on that, like anomaly detection
Re: Zero-Shot Forecasting: Our Search for a Time-Series Foundation Model
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Author here, apologies for not making it clear on the post regarding the definition of Zero-shot time-series forecasting, but it's quite widely used and here's the definition of it "Zero-shot time-series forecasting is a framework for time-series prediction that does not require fine-tuning with specific time-series data to be predicted."
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Re: Zero-Shot Forecasting: Our Search for a Time-Series Foundation Model
#9I wonder how this would perform on the M4 Makridakis competitions (time series competitions)
https://github.com/Mcompetitions/M4-methods
https://en.wikipedia.org/wiki/Makridakis_Competitions
Makridakis' conclusion remained true for many years: "statistically sophisticated and complex methods do not necessarily provide more accurate forecasts than simpler ones."
Maybe things have changed?
(side: Nixtla showed a simple ensemble outperforming Chronos, and the Chronos team responded, but there's some back and forth in the comments: https://www.linkedin.com/pulse/extended-comparison-chronos-a...)
Re: Zero-Shot Forecasting: Our Search for a Time-Series Foundation Model
#10Look i'm optimistic about time-series foundation models too, but this post is hard to take seriously when the test is so flawed:
- Forward filling missing short periods of missing values. Why keep this in when you explictly mention this is not normal? Either remove it all or don't impute anything
- Claiming superiority over classic models and then not mentioning any in the results table
- Or let's not forget, the cardinal sin of using MAPE as an evaluation metric