SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4126–4150 of 6748 papers

TitleStatusHype
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks—0
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series—0
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals—0
Unsupervised Deep Learning for IoT Time Series—0
Unsupervised detection of diachronic word sense evolution—0
Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training—0
Unsupervised Driving Event Discovery Based on Vehicle CAN-data—0
Unsupervised Event Coreference Resolution—0
Unsupervised Flood Detection on SAR Time Series—0
Unsupervised learning for economic risk evaluation in the context of Covid-19 pandemic—0
Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition—0
Unsupervised Learning through Temporal Smoothing and Entropy Maximization—0
Unsupervised model-free representation learning—0
Unsupervised non-parametric change point detection in quasi-periodic signals—0
Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks—0
Unsupervised Prediction of Negative Health Events Ahead of Time—0
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector—0
Unsupervised Representation Learning and Anomaly Detection in ECG Sequences—0
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version—0
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion—0
Unsupervised Visual Time-Series Representation Learning and Clustering—0
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series—0
Exploring Representations and Interventions in Time Series Foundation Models—0
Unveiling Early Warning Signals of Systemic Risks in Banks: A Recurrence Network-Based Approach—0
Unveiling the role of plasticity rules in reservoir computing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified