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AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
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AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data

Santosh Palaskar
Vijay Ekambaram
Arindam Jati
Neelamadhav Gantayat
Avirup Saha
Seema Nagar
Nam H. Nguyen
Pankaj Dayama
Renuka Sindhgatta
P. Mohapatra
Harshit Kumar
Jayant Kalagnanam
N. Hemachandra
N. Rangaraj
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Papers citing "AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data"

3 / 3 papers shown
Title
This Time is Different: An Observability Perspective on Time Series Foundation Models
This Time is Different: An Observability Perspective on Time Series Foundation Models
Ben Cohen
Emaad Khwaja
Youssef Doubli
Salahidine Lemaachi
Chris Lettieri
...
Kan Wang
Stephan Xie
David Asker
Ameet Talwalkar
Othmane Abou-Amal
AI4TSAI4CE
78
0
0
20 May 2025
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
Xu Liu
Juncheng Liu
Gerald Woo
Taha Aksu
Yuxuan Liang
Roger Zimmermann
Chenghao Liu
Silvio Savarese
Caiming Xiong
Doyen Sahoo
AI4TS
105
22
0
14 Oct 2024
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
Vijay Ekambaram
Arindam Jati
Pankaj Dayama
Sumanta Mukherjee
Nam H. Nguyen
Wesley M. Gifford
Chandra Reddy
Jayant Kalagnanam
AI4TSVLM
172
35
0
08 Jan 2024

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