145 research outputs found
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection
Dynamic time warping (DTW) is an effective dissimilarity measure in many time
series applications. Despite its popularity, it is prone to noises and
outliers, which leads to singularity problem and bias in the measurement. The
time complexity of DTW is quadratic to the length of time series, making it
inapplicable in real-time applications. In this paper, we propose a novel time
series dissimilarity measure named RobustDTW to reduce the effects of noises
and outliers. Specifically, the RobustDTW estimates the trend and optimizes the
time warp in an alternating manner by utilizing our designed temporal graph
trend filtering. To improve efficiency, we propose a multi-level framework that
estimates the trend and the warp function at a lower resolution, and then
repeatedly refines them at a higher resolution. Based on the proposed
RobustDTW, we further extend it to periodicity detection and outlier time
series detection. Experiments on real-world datasets demonstrate the superior
performance of RobustDTW compared to DTW variants in both outlier time series
detection and periodicity detection
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis
Time series anomaly detection is a challenging problem due to the complex
temporal dependencies and the limited label data. Although some algorithms
including both traditional and deep models have been proposed, most of them
mainly focus on time-domain modeling, and do not fully utilize the information
in the frequency domain of the time series data. In this paper, we propose a
Time-Frequency analysis based time series Anomaly Detection model, or TFAD for
short, to exploit both time and frequency domains for performance improvement.
Besides, we incorporate time series decomposition and data augmentation
mechanisms in the designed time-frequency architecture to further boost the
abilities of performance and interpretability. Empirical studies on widely used
benchmark datasets show that our approach obtains state-of-the-art performance
in univariate and multivariate time series anomaly detection tasks. Code is
provided at https://github.com/DAMO-DI-ML/CIKM22-TFAD.Comment: Accepted by the ACM International Conference on Information and
Knowledge Management (CIKM 2022
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
In real-world scenarios like traffic and energy, massive time-series data
with missing values and noises are widely observed, even sampled irregularly.
While many imputation methods have been proposed, most of them work with a
local horizon, which means models are trained by splitting the long sequence
into batches of fit-sized patches. This local horizon can make models ignore
global trends or periodic patterns. More importantly, almost all methods assume
the observations are sampled at regular time stamps, and fail to handle complex
irregular sampled time series arising from different applications. Thirdly,
most existing methods are learned in an offline manner. Thus, it is not
suitable for many applications with fast-arriving streaming data. To overcome
these limitations, we propose \ours: Bayesian Online Multivariate Time series
Imputation with functional decomposition. We treat the multivariate time series
as the weighted combination of groups of low-rank temporal factors with
different patterns. We apply a group of Gaussian Processes (GPs) with different
kernels as functional priors to fit the factors. For computational efficiency,
we further convert the GPs into a state-space prior by constructing an
equivalent stochastic differential equation (SDE), and developing a scalable
algorithm for online inference. The proposed method can not only handle
imputation over arbitrary time stamps, but also offer uncertainty
quantification and interpretability for the downstream application. We evaluate
our method on both synthetic and real-world datasets
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Time series anomaly detection is critical for a wide range of applications.
It aims to identify deviant samples from the normal sample distribution in time
series. The most fundamental challenge for this task is to learn a
representation map that enables effective discrimination of anomalies.
Reconstruction-based methods still dominate, but the representation learning
with anomalies might hurt the performance with its large abnormal loss. On the
other hand, contrastive learning aims to find a representation that can clearly
distinguish any instance from the others, which can bring a more natural and
promising representation for time series anomaly detection. In this paper, we
propose DCdetector, a multi-scale dual attention contrastive representation
learning model. DCdetector utilizes a novel dual attention asymmetric design to
create the permutated environment and pure contrastive loss to guide the
learning process, thus learning a permutation invariant representation with
superior discrimination abilities. Extensive experiments show that DCdetector
achieves state-of-the-art results on multiple time series anomaly detection
benchmark datasets. Code is publicly available at
https://github.com/DAMO-DI-ML/KDD2023-DCdetector
DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model
Electrical load forecasting is of great significance for the decision makings
in power systems, such as unit commitment and energy management. In recent
years, various self-supervised neural network-based methods have been applied
to electrical load forecasting to improve forecasting accuracy and capture
uncertainties. However, most current methods are based on Gaussian likelihood
methods, which aim to accurately estimate the distribution expectation under a
given covariate. This kind of approach is difficult to adapt to situations
where temporal data has a distribution shift and outliers. In this paper, we
propose a diffusion-based Seq2seq structure to estimate epistemic uncertainty
and use the robust additive Cauchy distribution to estimate aleatoric
uncertainty. Rather than accurately forecasting conditional expectations, we
demonstrate our method's ability in separating two types of uncertainties and
dealing with the mutant scenarios
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