What is recorded here?
Adapted lightweight mixer blocks for pretrained forecasting across heterogeneous temporal resolutions using adaptive patching, resolution sampling/prefixes and channel-independent pretraining followed by channel/exogenous mixing during fine-tuning.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
TSMixer backbone is inherited. Transfer claims concern specified forecasting datasets, not universal time-series guarantees.
Read the original work
- Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-shot Forecasting of Multivariate Time Series
- TTMs: Fast Multi-level Tiny Time Mixers for Improved Zero-shot and Few-shot Forecasting of Multivariate Time Series
- Ekambaram et al.: Tiny Time Mixers for transferable time-series forecasting — official research provenance
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Vijay Ekambaram; Arindam Jati; Pankaj Dayama; Sumanta Mukherjee; Nam H. Nguyen; Wesley M. Gifford; Chandra Reddy; Jayant Kalagnanam
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
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English catalogue narrative
Ekambaram et al.: Tiny Time Mixers for transferable time-series forecasting
Adapted lightweight mixer blocks for pretrained forecasting across heterogeneous temporal resolutions using adaptive patching, resolution sampling/prefixes and channel-independent pretraining followed by channel/exogenous mixing during fine-tuning.
Scientific scope
TSMixer backbone is inherited. Transfer claims concern specified forecasting datasets, not universal time-series guarantees.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Vijay Ekambaram; Arindam Jati; Pankaj Dayama; Sumanta Mukherjee; Nam H. Nguyen; Wesley M. Gifford; Chandra Reddy; Jayant Kalagnanam
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Original sources
- Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-shot Forecasting of Multivariate Time Series
- TTMs: Fast Multi-level Tiny Time Mixers for Improved Zero-shot and Few-shot Forecasting of Multivariate Time Series
- Ekambaram et al.: Tiny Time Mixers for transferable time-series forecasting — official research provenance