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Add KernelSynth training data

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  1. README.md +23 -7
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README.md CHANGED
@@ -919,7 +919,19 @@ dataset_info:
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  dataset_size: 57560596
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  license: Apache 2.0
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  homepage: https://github.com/mbohlkeschneider/gluon-ts/tree/mv_release/datasets
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- - config_name: tsmixup_10m
 
 
 
 
 
 
 
 
 
 
 
 
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  features:
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  - name: target
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  sequence: float64
@@ -1566,10 +1578,14 @@ configs:
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  data_files:
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  - split: train
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  path: taxi_30min/train-*
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- - config_name: tsmixup_10m
 
 
 
 
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  data_files:
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  - split: train
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- path: tsmixup_10m/train-*
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  - config_name: uber_tlc_daily
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  data_files:
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  - split: train
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  ds = datasets.load_dataset("autogluon/chronos_datasets", "weatherbench_hourly_temperature", streaming=True, split="train")
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  ```
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- ## Chronos training set with TSMixup
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- The dataset used for training the Chronos model (including KernelSynth synthetic data + TSMixup augmentations) can be loaded via the config `tsmixup_10m`
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  ```python
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- ds = datasets.load_dataset("autogluon/chronos_datasets", "tsmixup_10m", streaming=True, split="train")
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  ```
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- Note that since data in the `tsmixup_10m` config was obtained by combining various synthetic & real-world time series, the timestamps contain dummy values that have no connection to the original data.
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  ## License
 
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  dataset_size: 57560596
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  license: Apache 2.0
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  homepage: https://github.com/mbohlkeschneider/gluon-ts/tree/mv_release/datasets
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+ - config_name: training_corpus_kernel_synth_1m
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+ features:
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+ - name: target
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+ sequence: float64
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+ - name: id
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+ dtype: string
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+ - name: timestamp
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+ sequence: timestamp[ms]
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+ splits:
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+ - name: train
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+ num_examples: 1000000
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+ download_size: 8313239368
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+ - config_name: training_corpus_tsmixup_10m
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  features:
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  - name: target
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  sequence: float64
 
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  data_files:
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  - split: train
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  path: taxi_30min/train-*
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+ - config_name: training_corpus_kernel_synth_1m
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+ data_files:
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+ - split: train
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+ path: training_corpus/kernel_synth_1m/train-*
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+ - config_name: training_corpus_tsmixup_10m
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  data_files:
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  - split: train
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+ path: training_corpus/tsmixup_10m/train-*
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  - config_name: uber_tlc_daily
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  data_files:
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  - split: train
 
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  ds = datasets.load_dataset("autogluon/chronos_datasets", "weatherbench_hourly_temperature", streaming=True, split="train")
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  ```
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+ ## Chronos training corpus with TSMixup & KernelSynth
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+ The training corpus used for training the Chronos models can be loaded via the configs `training_corpus_tsmixup_10m` (10M TSMixup augmentations of real-world data) and `training_corpus_kernel_synth_1m` (1M synthetic time series generated with KernelSynth), e.g.,
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  ```python
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+ ds = datasets.load_dataset("autogluon/chronos_datasets", "training_corpus_tsmixup_10m", streaming=True, split="train")
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  ```
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+ Note that since data in the training corpus was obtained by combining various synthetic & real-world time series, the timestamps contain dummy values that have no connection to the original data.
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  ## License
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