Merlion: A Machine Learning Library for Time Series

2 code implementations • 20 Sep 2021 • Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang, Rowan Cassius, Doyen Sahoo, Devansh Arpit, Sri Subramanian, Gerald Woo, Amrita Saha, Arun Kumar Jagota, Gokulakrishnan Gopalakrishnan, Manpreet Singh, K C Krithika, Sukumar Maddineni, Daeki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven Hoi, Huan Wang

We introduce Merlion, an open-source machine learning library for time series.

Anomaly Detection BIG-bench Machine Learning +2

ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

2 code implementations • 3 Feb 2022 • Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Transformers have been actively studied for time-series forecasting in recent years.

Time Series Time Series Forecasting

Unified Training of Universal Time Series Forecasting Transformers

1 code implementation • 4 Feb 2024 • Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo

Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models.

Time Series Time Series Forecasting

Learning Deep Time-index Models for Time Series Forecasting

1 code implementation • 13 Jul 2022 • Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models.

Inductive Bias Meta-Learning +2

Model Agnostic Defence against Backdoor Attacks in Machine Learning

2 code implementations • 6 Aug 2019 • Sakshi Udeshi, Shanshan Peng, Gerald Woo, Lionell Loh, Louth Rawshan, Sudipta Chattopadhyay

In this work, we present NEO, a model agnostic framework to detect and mitigate such backdoor attacks in image classification ML models.

BIG-bench Machine Learning Decision Making +3

CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

1 code implementation • ICLR 2022 • Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step -- we justify such a paradigm from a causal perspective.

Contrastive Learning Representation Learning +2

Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain

1 code implementation • 8 Oct 2023 • Gerald Woo, Chenghao Liu, Akshat Kumar, Doyen Sahoo

Time series has been left behind in the era of pre-training and transfer learning.

Time Series Time Series Forecasting +1

AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

no code implementations • 10 Apr 2023 • Qian Cheng, Doyen Sahoo, Amrita Saha, Wenzhuo Yang, Chenghao Liu, Gerald Woo, Manpreet Singh, Silvio Saverese, Steven C. H. Hoi

There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency.