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context sequencelengths 9.93k 9.93k | forecast sequencelengths 20 20 | encoder_layer_0_ff_pre_norm sequencelengths 513 513 | encoder_layer_1_ff_pre_norm sequencelengths 513 513 | encoder_layer_2_ff_pre_norm sequencelengths 513 513 | encoder_layer_3_ff_pre_norm sequencelengths 513 513 |
|---|---|---|---|---|---|
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The dataset contains the activations from the feed-forward layers of a Chronos T5-tiny model (amazon/chronos-t5-tiny) when processing time series forecasting tasks.
Data Collection Process:
- The activations were extracted using forward hooks on the feed-forward layers of the Chronos T5 encoder blocks
- The model was run on the m4_daily train dataset from the autogluon/chronos_datasets collection
- Each sequence was left-padded with NaN values to ensure uniform length across the dataset
- Processing was done in batches of 128 samples to manage GPU memory constraints
- Activations were collected from pre-normalization points in each encoder feed-forward layer
- The time series from the m4_daily dataset are included for each activation
- The context window is 512 time steps + EOS token so 513 tokens total and the forecast is 14 time steps
Dataset Features:
- context: The padded input time series sequences from the m4_daily dataset
- forecast: The model's predictions for the next 14 time steps
- encoder_layer_X_ff_pre_norm: Activation tensors from the feed-forward layers before normalization for each encoder block X
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