Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning

Minbo Ma, Kai Tang, Huan Li, Fei Teng, Dalin Zhang, Tianrui Li

Abstract:

Multivariate Time Series Forecasting (MTSF) has long been a key research focus. Traditionally, these studies assume a fixed number
of variables, but in real-world applications, Cyber-Physical Systems often expand as new sensors are deployed, increasing variables in
MTSF. In light of this, we introduce a novel task, Expanding-variate Time Series Forecasting (EVTSF). This task presents unique challenges, specifically (1) handling inconsistent data shapes caused by adding new variables, and (2) addressing imbalanced spatiotemporal learning, where expanding variables have limited observed data due to the necessity for timely operation. To address
these challenges, we propose STEV, a flexible spatio-temporal forecasting framework. STEV includes a new Flat Scheme to tackle
the inconsistent data shape issue, which extends the graph-based spatio-temporal modeling architecture into 1D space by flattening
the 2D samples along the variable dimension, making the model variable-scale-agnostic while still preserving dynamic spatial correlations
through a holistic graph. Additionally, we introduce a novel Spatio-temporal Focal Learning strategy that incorporates a negative filter to resolve potential conflicts between contrastive learning and graph representation, and a focal contrastive loss as its core to guide the framework to focus on optimizing the expanding variables. To evaluate the effectiveness of STEV, we benchmark EVTSF
performance on three real-world datasets from various domains and compare it against three potential solutions employing state-of-the-art
(SOTA) MTSF models tailored for EVSTF. Experimental results show that STEV significantly outperforms its competitors, especially
in handling expanding variables. Notably, STEV, with only 5% of observations during the expanding period, is on par with SOTA
MTSF models trained with complete data. Further exploration of various expanding scenarios underscores the generalizability of
STEV in real-world applications.