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 2851–2875 of 6748 papers

TitleStatusHype
Object recognition for robotics from tactile time series data utilising different neural network architectures—0
DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation—0
Quantile-based fuzzy clustering of multivariate time series in the frequency domain—0
Highly Scalable and Provably Accurate Classification in Poincare BallsCode0
Mutation frequency time series reveal complex mixtures of clones in the world-wide SARS-CoV-2 viral population—0
Fractional Growth Portfolio Investment—0
Supervised DKRC with Images for Offline System Identification—0
Nonparametric Extrema Analysis in Time Series for Envelope Extraction, Peak Detection and Clustering—0
Accurate shape and phase averaging of time series through Dynamic Time Warping—0
Computer Vision Self-supervised Learning Methods on Time Series—0
A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection—0
Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples—0
Time Series Prediction using Deep Learning Methods in Healthcare—0
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures—0
Parallel Machine Learning for Forecasting the Dynamics of Complex Networks—0
Anomaly Detection on IT Operation Series via Online Matrix Profile—0
Sketches for Time-Dependent Machine LearningCode0
SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations—0
Magnetic Field Sensing for Pedestrian and Robot Indoor Positioning—0
Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems—0
Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach—0
Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models—0
DTWSSE: Data Augmentation with a Siamese Encoder for Time Series—0
Evolutionary Ensemble Learning for Multivariate Time Series Prediction—0
Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics—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