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.
Code (0)
등록된 구현이 없습니다.
Tasks
Time SeriesTime Series AnalysisTime Series ForecastingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Transformers in Time Series: A Survey
Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Among multiple advantages of Transforme…
Anomaly DetectionSurveyTime SeriesTime Series AnalysisSurvey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting
Alongside the continuous process of improving AI performance through the development of more sophisticated models, researchers have also focused their attention to the emerging concept of data-centric AI, which emphasize…
Time SeriesTime Series ForecastingsTransformer: A Modular Approach for Extracting Inter-Sequential and Temporal Information for Time-Series Forecasting
In recent years, numerous Transformer-based models have been applied to long-term time-series forecasting (LTSF) tasks. However, recent studies with linear models have questioned their effectiveness, demonstrating that s…
Time SeriesTime Series ForecastingYear-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review
This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 stud…
Deep LearningFeature EngineeringFraud DetectionPrivacy Preserving+1A Survey of Transformer Enabled Time Series Synthesis
Generative AI has received much attention in the image and language domains, with the transformer neural network continuing to dominate the state of the art. Application of these models to time series generation is less …
State Space ModelsSurveyTime SeriesTime Series Generation