125 citations · 184 across the 22 of their papers we have counts for
6 papers · 1 filter
SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting
Mingtian Tan, Palash Goyal, Mihir Parmar +6
Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical numerical observations insuffi…
LEAF: A Living Benchmark for Event-Augmented Forecasting
Mingtian Tan, Mihir Parmar, Palash Goyal +5
Large Language Models (LLMs) are increasingly applied to real-world forecasting tasks, yet evaluating their true predictive capability remains compromised by pre-training data cont…
STRIDE: Reasoning-Aware Training for Time Series Forecasting
Md Atik Ahamed, Mihir Parmar, Palash Goyal +4
Time Series Foundation Models (TSFMs) excel at numerical forecasting but operate as black boxes lacking qualitative reasoning. Conversely, applying LLMs directly to temporal data i…
TSMixer: An All-MLP Architecture for Time Series Forecasting
Si-An Chen, Chun-Liang Li, Nate Yoder +2
Real-world time-series datasets are often multivariate with complex dynamics. To capture this complexity, high capacity architectures like recurrent- or attention-based sequential…
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li +2
Semi-supervised anomaly detection is a common problem, as often the datasets containing anomalies are partially labeled. We propose a canonical framework: Semi-supervised Pseudo-la…
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection
Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li +3
Anomaly detection (AD), separating anomalies from normal data, has many applications across domains, from security to healthcare. While most previous works were shown to be effecti…