3,439 research outputs found
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Decomposing complex time series into trend, seasonality, and remainder
components is an important task to facilitate time series anomaly detection and
forecasting. Although numerous methods have been proposed, there are still many
time series characteristics exhibiting in real-world data which are not
addressed properly, including 1) ability to handle seasonality fluctuation and
shift, and abrupt change in trend and reminder; 2) robustness on data with
anomalies; 3) applicability on time series with long seasonality period. In the
paper, we propose a novel and generic time series decomposition algorithm to
address these challenges. Specifically, we extract the trend component robustly
by solving a regression problem using the least absolute deviations loss with
sparse regularization. Based on the extracted trend, we apply the the non-local
seasonal filtering to extract the seasonality component. This process is
repeated until accurate decomposition is obtained. Experiments on different
synthetic and real-world time series datasets demonstrate that our method
outperforms existing solutions.Comment: Accepted to the thirty-third AAAI Conference on Artificial
Intelligence (AAAI 2019), 9 pages, 5 figure
A Systematic Review for Transformer-based Long-term Series Forecasting
The emergence of deep learning has yielded noteworthy advancements in time
series forecasting (TSF). Transformer architectures, in particular, have
witnessed broad utilization and adoption in TSF tasks. Transformers have proven
to be the most successful solution to extract the semantic correlations among
the elements within a long sequence. Various variants have enabled transformer
architecture to effectively handle long-term time series forecasting (LTSF)
tasks. In this article, we first present a comprehensive overview of
transformer architectures and their subsequent enhancements developed to
address various LTSF tasks. Then, we summarize the publicly available LTSF
datasets and relevant evaluation metrics. Furthermore, we provide valuable
insights into the best practices and techniques for effectively training
transformers in the context of time-series analysis. Lastly, we propose
potential research directions in this rapidly evolving field
Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services
Time series anomaly detection is crucial for industrial monitoring services
that handle a large volume of data, aiming to ensure reliability and optimize
system performance. Existing methods often require extensive labeled resources
and manual parameter selection, highlighting the need for automation. This
paper proposes a comprehensive framework for automatic parameter optimization
in time series anomaly detection models. The framework introduces three
optimization targets: prediction score, shape score, and sensitivity score,
which can be easily adapted to different model backbones without prior
knowledge or manual labeling efforts. The proposed framework has been
successfully applied online for over six months, serving more than 50,000 time
series every minute. It simplifies the user's experience by requiring only an
expected sensitive value, offering a user-friendly interface, and achieving
desired detection results. Extensive evaluations conducted on public datasets
and comparison with other methods further confirm the effectiveness of the
proposed framework.Comment: Accepted by 2023 IJCAI Worksho
A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
Recently, frequency transformation (FT) has been increasingly incorporated
into deep learning models to significantly enhance state-of-the-art accuracy
and efficiency in time series analysis. The advantages of FT, such as high
efficiency and a global view, have been rapidly explored and exploited in
various time series tasks and applications, demonstrating the promising
potential of FT as a new deep learning paradigm for time series analysis.
Despite the growing attention and the proliferation of research in this
emerging field, there is currently a lack of a systematic review and in-depth
analysis of deep learning-based time series models with FT. It is also unclear
why FT can enhance time series analysis and what its limitations in the field
are. To address these gaps, we present a comprehensive review that
systematically investigates and summarizes the recent research advancements in
deep learning-based time series analysis with FT. Specifically, we explore the
primary approaches used in current models that incorporate FT, the types of
neural networks that leverage FT, and the representative FT-equipped models in
deep time series analysis. We propose a novel taxonomy to categorize the
existing methods in this field, providing a structured overview of the diverse
approaches employed in incorporating FT into deep learning models for time
series analysis. Finally, we highlight the advantages and limitations of FT for
time series modeling and identify potential future research directions that can
further contribute to the community of time series analysis
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