6 citations · 7 across the 13 of their papers we have counts for
8 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…
HyperMLP: An Integrated Perspective for Sequence Modeling
Jiecheng Lu, Shihao Yang
Self-attention is often viewed as probabilistic query-key lookup, motivating designs that preserve normalized attention scores and fixed positional semantics. We advocate a simpler…
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…
Linear Transformers as VAR Models: Aligning Autoregressive Attention Mechanisms with Autoregressive Forecasting
Jiecheng Lu, Shihao Yang
Autoregressive attention-based time series forecasting (TSF) has drawn increasing interest, with mechanisms like linear attention sometimes outperforming vanilla attention. However…
WAVE: Weighted Autoregressive Varying Gate for Time Series Forecasting
Jiecheng Lu, Xu Han, Yan Sun +1
We propose a Weighted Autoregressive Varying gatE (WAVE) attention mechanism equipped with both Autoregressive (AR) and Moving-average (MA) components. It can adapt to various atte…
In-context Time Series Predictor
Jiecheng Lu, Yan Sun, Shihao Yang
Recent Transformer-based large language models (LLMs) demonstrate in-context learning ability to perform various functions based solely on the provided context, without updating mo…