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Copy file name to clipboardexpand all lines: README.md
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## Checkpoint timesfm-1.0-200m (-pytorch)
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timesfm-1.0-200m is the first open model checkpoint:
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timesfm-1.0-200m is our first open model checkpoint:
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- It performs univariate time series forecasting for context lengths up to 512 timepoints and any horizon lengths, with an optional frequency indicator.
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- It focuses on point forecasts, and does not support probabilistic forecasts. We experimentally offer quantile heads but they have not been calibrated after pretraining.
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- It requires the context to be contiguous (i.e. no "holes"), and the context and the horizon to be of the same frequency.
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## Checkpoint timesfm-2.0-500m (-jax/-pytorch)
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timesfm-2.0-500m is the second open model checkpoint:
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timesfm-2.0-500m is our second open model checkpoint:
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- It performs univariate time series forecasting for context lengths up to 2048 timepoints and any horizon lengths, with an optional frequency indicator.
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- It focuses on point forecasts. We experimentally offer 10 quantile heads but they have not been calibrated after pretraining.
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- It requires the context to be contiguous (i.e. no "holes"), and the context and the horizon to be of the same frequency.
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