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 1801–1825 of 6748 papers

TitleStatusHype
Evaluation of post-hoc interpretability methods in time-series classificationCode1
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data—0
Wind power ramp prediction algorithm based on wavelet deep belief network—0
Active Privacy-Utility Trade-off Against Inference in Time-Series Data Sharing—0
Learning Latent Causal Dynamics—0
Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data—0
Case-based reasoning for rare events prediction on strategic sites—0
Ketamine-Medetomidine General Anesthesia Occurs With Alternation of Cortical Electrophysiological Activity Between High and Low Complex States—0
Spectral Propagation Graph Network for Few-shot Time Series Classification—0
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion—0
The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting—0
Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods—0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
Detecting Anomalies within Time Series using Local Neural TransformationsCode1
Time Series Anomaly Detection by Cumulative Radon FeaturesCode1
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting—0
Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model—0
Structured Time Series Prediction without Structural PriorCode1
Detecting Structural Breaks in Foreign Exchange Markets by using the group LASSO technique—0
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time seriesCode0
TACTiS: Transformer-Attentional Copulas for Time SeriesCode1
Machine Learning Models in Stock Market Prediction—0
Advanced sleep spindle identification with neural networksCode1
Robust Anomaly Detection for Time-series Data—0
TTS-GAN: A Transformer-based Time-Series Generative Adversarial NetworkCode2
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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