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 3376–3400 of 6748 papers

TitleStatusHype
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks—0
Composable Generative Models—0
Joint Characterization of Multiscale Information in High Dimensional Data—0
Identification of Phase-Locked Loop System From Its Experimental Time Series—0
Robust Domain-Free Domain Generalization with Class-aware Alignment—0
Analysis of EEG data using complex geometric structurization—0
Testing for Nonlinear Cointegration under Heteroskedasticity—0
Performance Dependency of LSTM and NAR Beamformers With Respect to Sensor Array Properties in V2I Scenario—0
Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters—0
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification—0
POLA: Online Time Series Prediction by Adaptive Learning Rates—0
Classification of multivariate weakly-labelled time-series with attentionCode0
On the use of generative deep neural networks to synthesize artificial multichannel EEG signals—0
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation—0
Pattern Sampling for Shapelet-based Time Series Classification—0
Dynamic Virtual Graph Significance Networks for Predicting InfluenzaCode0
An Operator Theoretic Approach for Analyzing Sequence Neural NetworksCode0
A Complex Systems Approach To Feature Extraction for Chaotic Behavior RecognitionCode0
Geometric feature performance under downsampling for EEG classification tasks—0
Controlling False Discovery Rates under Cross-Sectional Correlations—0
Tight lower bounds for Dynamic Time WarpingCode0
On Technical Trading and Social Media Indicators in Cryptocurrencies' Price Classification Through Deep Learning—0
Clustering Interval-Censored Time-Series for Disease Phenotyping—0
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification—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