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 2926–2950 of 6748 papers

TitleStatusHype
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds—0
Functional Classwise Principal Component Analysis: A Novel Classification Framework—0
Geometric feature performance under downsampling for EEG classification tasks—0
Functional Bayesian Filter—0
Geometric Optimisation on Manifolds with Applications to Deep Learning—0
Functional Annotation of Human Cognitive States using Graph Convolution Networks—0
GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users—0
Conditional independence testing with a single realization of a multivariate nonstationary nonlinear time series—0
A Robust Score-Driven Filter for Multivariate Time Series—0
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings—0
GG-SSMs: Graph-Generating State Space Models—0
Global Constraint Catalog, Volume II, Time-Series Constraints—0
Global Flood Prediction: a Multimodal Machine Learning Approach—0
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series—0
Conditional heteroskedasticity in crypto-asset returns—0
Fully convolutional networks for structural health monitoring through multivariate time series classification—0
Conditional Generative Models for Counterfactual Explanations—0
A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow—0
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models—0
GP Kernels for Cross-Spectrum Analysis—0
From time-series transcriptomics to gene regulatory networks: a review on inference methods—0
Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution—0
GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model—0
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics—0
From Time Series to Euclidean Spaces: On Spatial Transformations for Temporal Clustering—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