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 1701–1725 of 6748 papers

TitleStatusHype
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems—0
Comprehensive Review of Neural Differential Equations for Time Series Analysis—0
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach—0
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions—0
Accurate shape and phase averaging of time series through Dynamic Time Warping—0
Composition Properties of Inferential Privacy for Time-Series Data—0
Deep Baseline Network for Time Series Modeling and Anomaly Detection—0
Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory—0
Deep Bayesian Nonparametric Tracking—0
Deep Canonically Correlated LSTMs—0
Deep Canonical Time Warping—0
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information—0
Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning—0
Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis—0
Deep Classification of Epileptic Signals—0
Deep Sequence Learning for Accurate Gestational Age Estimation from a \$25 Doppler Device—0
A review on outlier/anomaly detection in time series data—0
Composite FORCE learning of chaotic echo state networks for time-series prediction—0
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images—0
Deep Convolutional Neural Network for Non-rigid Image Registration—0
Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification—0
A Method for Massively Parallel Analysis of Time Series—0
Deep COVID-19 Forecasting for Multiple States with Data Augmentation—0
Composable Generative Models—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