4 papers · 1 filter
Beyond Similarity: Temporal Operator Attention for Time Series Analysis
Jevon Twitty, Vinh Pham, Nitiwith Rotchanarak +4
A persistent paradox in time-series forecasting is that structurally simple MLP and linear models often outperform high-capacity Transformers. We argue that this gap arises from a…
StretchTime: Adaptive Time Series Forecasting via Symplectic Attention
Yubin Kim, Viresh Pati, Jevon Twitty +3
Transformer architectures have established strong baselines in time series forecasting, yet they typically rely on positional encodings that assume uniform, index-based temporal pr…
ZeroS: Zero-Sum Linear Attention for Efficient Transformers
Jiecheng Lu, Xu Han, Yan Sun +4
Linear attention methods offer Transformers complexity but typically underperform standard softmax attention. We identify two fundamental limitations affecting these approac…
CAPS: Unifying Attention, Recurrence, and Alignment in Transformer-based Time Series Forecasting
Viresh Pati, Yubin Kim, Vinh Pham +3
This paper presents (Clock-weighted Aggregation with Prefix-products and Softmax), a structured attention mechanism for time series forecasting that decouples three…