time-series-10

Time Series Alignment

3260 papers • 126 benchmarks • 313 datasets

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Artificial signal data for signal alignment testing

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Most implemented papers

CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning

Peiyuan Liu, Hang Guo, Naiqi Li, Jigang Bao, Xudong Ren, Yong Jiang, Shu-Tao Xia, Tao Dai•Mon Mar 11 2024

A novel Cross-Modal LLM Fine-Tuning (CALF) framework for MTSF is proposed that establishes state-of-the-art performance for both long-term and short-term forecasting tasks with low computational complexity, and exhibits favorable few-shot and zero-shot abilities similar to that in LLMs.

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Time Series Alignment with Global Invariances

Rémi Flamary, N. Courty, R. Tavenard, Titouan Vayer, L. Chapel, Yann Soullard•Sun Feb 09 2020

This work addresses the problem of comparing time series while taking into account both feature space transformation and temporal variability, and proposes a latent global transformation of the feature space with the widely used Dynamic Time Warping (DTW).

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Closed-Form Diffeomorphic Transformations for Time Series Alignment

Iñigo Martinez, E. Viles, Igor García Olaizola•Wed Jun 15 2022

This work presents a closed-form expression for the ODE solution and its gradient under continuous piecewise-affine (CPA) velocity functions and presents a highly optimized implementation of the results on CPU and GPU.

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Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry

Ferdinand Rewicki, J. Gawlikowski, Julia Niebling, Joachim Denzler•Thu Jun 13 2024

The detection of abnormal or critical system states is essential in condition monitoring. While much attention is given to promptly identifying anomalies, a retrospective analysis of these anomalies can significantly enhance our comprehension of the underlying causes of observed undesired behavior. This aspect becomes particularly critical when the monitored system is deployed in a vital environment. In this study, we delve into anomalies within the domain of Bio-Regenerative Life Support Systems (BLSS) for space exploration and analyze anomalies found in telemetry data stemming from the EDEN ISS space greenhouse in Antarctica. We employ time series clustering on anomaly detection results to categorize various types of anomalies in both uni- and multivariate settings. We then assess the effectiveness of these methods in identifying systematic anomalous behavior. Additionally, we illustrate that the anomaly detection methods MDI and DAMP produce complementary results, as previously indicated by research.

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