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 2626–2650 of 6748 papers

TitleStatusHype
Temporal Graph Signal Decomposition—0
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series ForecastingCode2
Neural ODE to model and prognose thermoacoustic instability—0
Domain-guided Machine Learning for Remotely Sensed In-Season Crop Growth Estimation—0
MegazordNet: combining statistical and machine learning standpoints for time series forecasting—0
Beyond Predictions in Neural ODEs: Identification and Interventions—0
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection—0
Continuous-Time Deep Glioma Growth ModelsCode1
Machine learning structure preserving brackets for forecasting irreversible processes—0
Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical ModelCode1
Unsupervised Speech Enhancement using Dynamical Variational Auto-EncodersCode1
Approximate Bayesian Computation with Path SignaturesCode0
STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised LearningCode1
Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization—0
Residual Networks as Flows of Velocity Fields for Diffeomorphic Time Series Alignment—0
Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time SeriesCode0
Attention-based Neural Network for Driving Environment Complexity Perception—0
Neural Controlled Differential Equations for Online Prediction TasksCode1
A mathematical perspective on edge-centric brain functional connectivityCode0
Experimentally testable whole brain manifolds that recapitulate behavior—0
Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences—0
GPLA-12: An Acoustic Signal Dataset of Gas Pipeline LeakageCode1
TS2Vec: Towards Universal Representation of Time SeriesCode1
pyWATTS: Python Workflow Automation Tool for Time SeriesCode1
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian ProcessesCode0
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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