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  1. README.md +5 -3
README.md CHANGED
@@ -174,7 +174,7 @@ fewshot_output = finetune_forecast_trainer.evaluate(dset_test)
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  ## Training Data
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- The TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:
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  - Australian Electricity Demand: https://zenodo.org/records/4659727
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  - Australian Weather: https://zenodo.org/records/4654822
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  - Bitcoin dataset: https://zenodo.org/records/5122101
@@ -187,10 +187,12 @@ The TTM models were trained on a collection of datasets from the Monash Time Ser
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  - US Births: https://zenodo.org/records/4656049
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  - Wind Farms Production data: https://zenodo.org/records/4654858
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  - Wind Power: https://zenodo.org/records/4656032
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- - [to be updated]
 
 
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- ## Citation [optional]
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  Kindly cite the following paper, if you intend to use our model or its associated architectures/approaches in your
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  work
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  ## Training Data
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+ The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:
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  - Australian Electricity Demand: https://zenodo.org/records/4659727
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  - Australian Weather: https://zenodo.org/records/4654822
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  - Bitcoin dataset: https://zenodo.org/records/5122101
 
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  - US Births: https://zenodo.org/records/4656049
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  - Wind Farms Production data: https://zenodo.org/records/4654858
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  - Wind Power: https://zenodo.org/records/4656032
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+ In addition to the above datasets, the updated TTM model (512-96-r2) was trained on the following:
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+ - PEMSD3, PEMSD4, PEMSD7, PEMSD8, PEMS_BAY: https://drive.google.com/drive/folders/1g5v2Gq1tkOq8XO0HDCZ9nOTtRpB6-gPe
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+ - LOS_LOOP: https://drive.google.com/drive/folders/1g5v2Gq1tkOq8XO0HDCZ9nOTtRpB6-gPe
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+ ## Citation
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  Kindly cite the following paper, if you intend to use our model or its associated architectures/approaches in your
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  work
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