The goal of sequential event prediction is to estimate the next event based on a sequence of historical events, with applications to sequential recommendation, user behavior analysis and clinical treatment.
Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data.
Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting. However, those transformer-based models suffer a severe deterioration performance with prolonged input length, which prohibits them from using extended historical info. Moreover, these methods tend to handle complex examples in long-term forecasting with increased model complexity, which often leads to a significant increase in computation and less robustness in performance(e. g., overfitting).
Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications.
Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information.
Ranked #2 on Time Series Forecasting on ETTh1 (336)
Efficient load forecasting is needed to ensure better observability in the distribution networks, whereas such forecasting is made possible by an increasing number of smart meter installations.
Localizing the root cause of network faults is crucial to network operation and maintenance.
To the best of our knowledge, this paper is the first work to comprehensively and systematically summarize the recent advances of Transformers for modeling time series data.
Although Transformer-based methods have significantly improved state-of-the-art results for long-term series forecasting, they are not only computationally expensive but more importantly, are unable to capture the global view of time series (e. g. overall trend).
As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms which constitute the infrastructure of Alibaba Cloud.
By incorporating the learned long-range structure, the second stage can enhance the prediction accuracy in the forecast horizon.
In this paper, we systematically review different data augmentation methods for time series.
Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system.
It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.
Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection.
Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component.