5,745 research outputs found
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Multivariate time series (MTS) forecasting is widely used in various domains,
such as meteorology and traffic. Due to limitations on data collection,
transmission, and storage, real-world MTS data usually contains missing values,
making it infeasible to apply existing MTS forecasting models such as linear
regression and recurrent neural networks. Though many efforts have been devoted
to this problem, most of them solely rely on local dependencies for imputing
missing values, which ignores global temporal dynamics. Local
dependencies/patterns would become less useful when the missing ratio is high,
or the data have consecutive missing values; while exploring global patterns
can alleviate such problems. Thus, jointly modeling local and global temporal
dynamics is very promising for MTS forecasting with missing values. However,
work in this direction is rather limited. Therefore, we study a novel problem
of MTS forecasting with missing values by jointly exploring local and global
temporal dynamics. We propose a new framework LGnet, which leverages memory
network to explore global patterns given estimations from local perspectives.
We further introduce adversarial training to enhance the modeling of global
temporal distribution. Experimental results on real-world datasets show the
effectiveness of LGnet for MTS forecasting with missing values and its
robustness under various missing ratios.Comment: Accepted by AAAI 202
Graph Convolutional Networks for Traffic Forecasting with Missing Values
Traffic forecasting has attracted widespread attention recently. In reality,
traffic data usually contains missing values due to sensor or communication
errors. The Spatio-temporal feature in traffic data brings more challenges for
processing such missing values, for which the classic techniques (e.g., data
imputations) are limited: 1) in temporal axis, the values can be randomly or
consecutively missing; 2) in spatial axis, the missing values can happen on one
single sensor or on multiple sensors simultaneously. Recent models powered by
Graph Neural Networks achieved satisfying performance on traffic forecasting
tasks. However, few of them are applicable to such a complex missing-value
context. To this end, we propose GCN-M, a Graph Convolutional Network model
with the ability to handle the complex missing values in the Spatio-temporal
context. Particularly, we jointly model the missing value processing and
traffic forecasting tasks, considering both local Spatio-temporal features and
global historical patterns in an attention-based memory network. We propose as
well a dynamic graph learning module based on the learned local-global
features. The experimental results on real-life datasets show the reliability
of our proposed method.Comment: To appear in Data Mining and Knowledge Discovery (DMKD), Springe
Graph Deep Learning for Time Series Forecasting
Graph-based deep learning methods have become popular tools to process
collections of correlated time series. Differently from traditional
multivariate forecasting methods, neural graph-based predictors take advantage
of pairwise relationships by conditioning forecasts on a (possibly dynamic)
graph spanning the time series collection. The conditioning can take the form
of an architectural inductive bias on the neural forecasting architecture,
resulting in a family of deep learning models called spatiotemporal graph
neural networks. Such relational inductive biases enable the training of global
forecasting models on large time-series collections, while at the same time
localizing predictions w.r.t. each element in the set (i.e., graph nodes) by
accounting for local correlations among them (i.e., graph edges). Indeed,
recent theoretical and practical advances in graph neural networks and deep
learning for time series forecasting make the adoption of such processing
frameworks appealing and timely. However, most of the studies in the literature
focus on proposing variations of existing neural architectures by taking
advantage of modern deep learning practices, while foundational and
methodological aspects have not been subject to systematic investigation. To
fill the gap, this paper aims to introduce a comprehensive methodological
framework that formalizes the forecasting problem and provides design
principles for graph-based predictive models and methods to assess their
performance. At the same time, together with an overview of the field, we
provide design guidelines, recommendations, and best practices, as well as an
in-depth discussion of open challenges and future research directions
Multi-scale Attention Flow for Probabilistic Time Series Forecasting
The probability prediction of multivariate time series is a notoriously
challenging but practical task. On the one hand, the challenge is how to
effectively capture the cross-series correlations between interacting time
series, to achieve accurate distribution modeling. On the other hand, we should
consider how to capture the contextual information within time series more
accurately to model multivariate temporal dynamics of time series. In this
work, we proposed a novel non-autoregressive deep learning model, called
Multi-scale Attention Normalizing Flow(MANF), where we integrate multi-scale
attention and relative position information and the multivariate data
distribution is represented by the conditioned normalizing flow. Additionally,
compared with autoregressive modeling methods, our model avoids the influence
of cumulative error and does not increase the time complexity. Extensive
experiments demonstrate that our model achieves state-of-the-art performance on
many popular multivariate datasets
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