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When I build a non-foundation time series, I have clearly demarcated training and testing sets. But with foundation models, how can I apply the model to a backdated time series ? Technically it may be easy to do this but how can I be sure that the model is not cheating ? To be sure that there is no cheating taking place, the makes of the foundation model need to open source all the datasets that were used to train the model. Once I am sure that the y variable I am predicting on is not a part of the foundation models training dataset (or related y variables), then I can say with complete certainty that the model can be applied for periods back in time. Can I see which all datasets have been used to train TimeGPT ? Moirai (Salesforce) have given a breakdown of which categories of time series data sets (finance, energy etc) they have used to train their model, in one of the papers.
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This is a good question! At this moment we're not able to share the details around the datasets we use. If you forecast the unknown future, you're sure that data wasn't used in training TimeGPT. Also, if you use a proprietary dataset, it is obviously also not part of our training curriculum, so if you'd like to be completely certain that there is no 'cheating' going on, that may be the way to go for you.
When I build a non-foundation time series, I have clearly demarcated training and testing sets. But with foundation models, how can I apply the model to a backdated time series ? Technically it may be easy to do this but how can I be sure that the model is not cheating ? To be sure that there is no cheating taking place, the makes of the foundation model need to open source all the datasets that were used to train the model. Once I am sure that the y variable I am predicting on is not a part of the foundation models training dataset (or related y variables), then I can say with complete certainty that the model can be applied for periods back in time. Can I see which all datasets have been used to train TimeGPT ? Moirai (Salesforce) have given a breakdown of which categories of time series data sets (finance, energy etc) they have used to train their model, in one of the papers.
The text was updated successfully, but these errors were encountered: