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 5751–5775 of 6748 papers

TitleStatusHype
Framework for Inferring Following Strategies from Time Series of Movement DataCode0
Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death ProcessesCode0
Action Recognition Using Volumetric Motion RepresentationsCode0
Data Consistency Approach to Model ValidationCode0
The DeepCAR Method: Forecasting Time-Series Data That Have Change PointsCode0
DSANet: Dual Self-Attention Network for Multivariate Time Series ForecastingCode0
Dropout Feature Ranking for Deep Learning ModelsCode0
Neural Pharmacodynamic State Space ModelingCode0
Inferring Multidimensional Rates of Aging from Cross-Sectional DataCode0
Inferring network connectivity from event timing patternsCode0
Inferring species interactions using Granger causality and convergent cross mappingCode0
Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural NetworkCode0
Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme DiscoveryCode0
Neural SDEs for Conditional Time Series Generation and the Signature-Wasserstein-1 metricCode0
Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal AnalysisCode0
Neural State-Space Modeling with Latent Causal-Effect DisentanglementCode0
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signalCode0
Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time SeriesCode0
A bootstrap test to detect prominent Granger-causalities across frequenciesCode0
Deep Learning and Linear Programming for Automated Ensemble Forecasting and InterpretationCode0
Domain Adaptation with Representation Learning and Nonlinear Relation for Time SeriesCode0
NeuralWarp: Time-Series Similarity with Warping NetworksCode0
Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor StreamsCode0
Neuronal architecture extracts statistical temporal patternsCode0
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one MapsCode0
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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