Row 8092

Row ID: 8092 | Dataset Entry | Axioma AXP Content Repository

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I've been exploring foundational time series models like TimeGPT, Moirai, Chronos, etc., and wonder if they truly have the potential for powerfully sample-efficient forecasting or if they're just borrowing the hype from foundational models in NLP and bringing it to the time series domain.

I can see why they might work, for example, in demand forecasting, where it's about identifying trends, cycles, etc. But can they handle arbitrary time series data like environmental monitoring, financial markets, or biomedical signals, which have irregular patterns and non-stationary data?

Is their ability to generalize overestimated?

FieldValue
text I've been exploring foundational time series models like TimeGPT, Moirai, Chronos, etc., and wonder if they truly have the potential for powerfully sample-efficient forecasting or if they're just borrowing the hype from foundational models in NLP and bringing it to the time series domain. I can see why they might work, for example, in demand forecasting, where it's about identifying trends, cycles, etc. But can they handle arbitrary time series data like environmental monitoring, financial mark…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-18
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url_encoded Z0FBQUFBQm5Lak9IUkJENnlkcWMzLWZJbXMyTUJrQmk0b2draW0xNG5UcGJlUkRsMmJsZzFUcFNMVEZ2SXI2WnhDaFpGWHNrNVhCb3d5NklFVlRZa3Y3eTB3bjdobDJndlVrMWo2WUdobTVUaVFjakNyVzhmR2M5ajZ6Z0ZyUEtfZEFqMEgtaUhCellGOEJ0UW02SXAwZURNSFpQNzkyN2hDSktpa0NXckY0UzU5YnZqcVpBQnJVVC1zOTV1T1FEeUs2WGhhLVR5d0pUN1RUTktRY2RJYk1LUG1TdkhEeXVVdz09

Raw Record

{
  "text": "I've been exploring foundational time series models like TimeGPT, Moirai, Chronos, etc., and wonder if they truly have the potential for powerfully sample-efficient forecasting or if they're just borrowing the hype from foundational models in NLP and bringing it to the time series domain.\n\nI can see why they might work, for example, in demand forecasting, where it's about identifying trends, cycles, etc. But can they handle arbitrary time series data like environmental monitoring, financial markets, or biomedical signals, which have irregular patterns and non-stationary data?\n\nIs their ability to generalize overestimated?",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-18",
  "username_encoded": "Z0FBQUFBQm5LakwzZ0F4WEtYOVdPcml4VEl3ZS1xamFQWF9sT0t2NzVaMUxVeGNhT2x6bFJpNklNMjBva05QZGxyeHZMaTV0aGRNVVNCU18yMXNiLUFmbGFVcUJtZ3pTMlE9PQ==",
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}
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