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 6026–6050 of 6748 papers

TitleStatusHype
Detecting structural perturbations from time series with deep learningCode0
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical FeaturesCode0
Benchmark of Deep Learning Models on Large Healthcare MIMIC DatasetsCode0
Learning compressed representations of blood samples time series with missing dataCode0
Unsupervised Learning for Computational PhenotypingCode0
Multimodal Transformer for Unaligned Multimodal Language SequencesCode0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
A 1d convolutional network for leaf and time series classificationCode0
Learning Deep Input-Output Stable DynamicsCode0
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product NetworksCode0
Benchmarking time series classification -- Functional data vs machine learning approachesCode0
Graph Edit NetworksCode0
Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural NetworksCode0
Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural NetworkCode0
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural NetworkCode0
Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time SeriesCode0
Twitter conversations predict the daily confirmed COVID-19 casesCode0
On the Metrics and Adaptation Methods for Domain Divergences of sEMG-based Gesture RecognitionCode0
Learning Efficient Representations of Mouse Movements to Predict User AttentionCode0
The UCR Time Series ArchiveCode0
Granger Causality using Neural NetworksCode0
Learning filter widths of spectral decompositions with waveletsCode0
Step Counting with Attention-based LSTMCode0
Gradient-free training of recurrent neural networksCode0
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly DetectionCode0
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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