2 citations · 3 across the 14 of their papers we have counts for
3 papers · 1 filter
LEAF: A Living Benchmark for Event-Augmented Forecasting
Mingtian Tan, Mihir Parmar, Palash Goyal +5
Large Language Models (LLMs) are increasingly applied to forecasting. To evaluate this capability while mitigating pre-training data contamination, several living benchmarks have b…
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…
Synapse: Adaptive Arbitration of Complementary Expertise in Time Series Foundational Models
Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar +7
Pre-trained Time Series Foundational Models (TSFMs) represent a significant advance, capable of forecasting diverse time series with complex characteristics, including varied seaso…