NeurIPS 2025

TIMEPERCEIVER: An Encoder-Decoder Framework
for Generalized Time-Series Forecasting
Jaebin Lee
Sungkyunkwan University
jaebin.lee@skku.edu
Hankook Lee
Sungkyunkwan University
hankook.lee@skku.edu
Abstract
In machine learning, effective modeling requires a holistic consideration of how
to encode inputs, make predictions (i.e., decoding), and train the model. How-
ever, in time-series forecasting, prior work has predominantly focused on encoder
design, often treating prediction and training as separate or secondary concerns.
In this paper, we propose TIMEPERCEIVER, a unified encoder-decoder forecast-
ing framework that is tightly aligned with an effective training strategy. To be
specific, we first generalize the forecasting task to include diverse temporal pre-
diction objectives such as extrapolation, interpolation, and imputation. Since this
generalization requires handling input and target segments that are arbitrarily po-
sitioned along the temporal axis, we design a novel encoder-decoder architecture
that can flexibly perceive and adapt to these varying positions. For encoding,
we introduce a set of latent bottleneck representations that can interact with all
input segments to jointly capture temporal and cross-channel dependencies. For
decoding, we leverage learnable queries corresponding to target timestamps to
effectively retrieve relevant information. Extensive experiments demonstrate that
our framework consistently and significantly outperforms prior state-of-the-art
baselines across a wide range of benchmark datasets. The code is available at
https://github.com/efficient-learning-lab/TimePerceiver.
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Introduction
Time-series forecasting is a fundamental task in machine learning, aiming to predict future events
based on past observations. It is of practical importance, as it plays a crucial role in many real-world
applications, including weather forecasting [1], electricity consumption forecasting [2], and traffic
flow prediction [3]. Despite decades of rap...