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Found 8 papers, 2 papers with code
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
Consequently, there has been a recent surge in utilizing pre-trained large language models (LLMs) with token adaptations for TS forecasting.
AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data.
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
TSMixer outperforms state-of-the-art MLP and Transformer models in forecasting by a considerable margin of 8-60%.
Multivariate Time Series Forecasting
Representation Learning
+2
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values.
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models.
Ranked #1 on
Time Series Forecasting
on Electricity (192)
Multivariate Time Series Forecasting
Representation Learning
+1
Interpretable Clustering via Multi-Polytope Machines
To solve our formulation we propose a two phase approach where we first initialize clusters and polytopes using alternating minimization, and then use coordinate descent to boost clustering performance.
Optimal Generalized Decision Trees via Integer Programming
Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features.
A Comparison of Decision Analysis and Expert Rules for Sequential Diagnosis
We report an experimental comparison of the performance of the two approaches to troubleshooting, specifically to test selection for fault diagnosis.
