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 6476–6500 of 6748 papers

TitleStatusHype
Taylor's law for Human Linguistic SequencesCode0
Evolving-Graph Gaussian ProcessesCode0
Deep Learning for Human Locomotion Analysis in Lower-Limb Exoskeletons: A Comparative StudyCode0
ShortFuse: Biomedical Time Series Representations in the Presence of Structured InformationCode0
Automatic Anomaly Detection in the Cloud Via Statistical LearningCode0
Deep learning for gravitational-wave data analysis: A resampling white-box approachCode0
Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP EventsCode0
Modeling emotion in complex stories: the Stanford Emotional Narratives DatasetCode0
TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point ClusteringCode0
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular SamplingCode0
Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costsCode0
Modeling Irregularly Sampled Clinical Time SeriesCode0
Event Detection via Probability Density Function RegressionCode0
Short-term daily precipitation forecasting with seasonally-integrated autoencoderCode0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal DataCode0
Using Clinical Drug Representations for Improving Mortality and Length of Stay PredictionsCode0
Deep learning-based electroencephalography analysis: a systematic reviewCode0
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for BrazilCode0
CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline SummarizationCode0
Your time series is worth a binary image: machine vision assisted deep framework for time series forecastingCode0
Automated Deep Abstractions for Stochastic Chemical Reaction NetworksCode0
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden ConfoundersCode0
Using Clinical Notes with Time Series Data for ICU ManagementCode0
TEASER: Early and Accurate Time Series ClassificationCode0
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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