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

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Showing 2851–2900 of 6748 papers

TitleStatusHype
Confident Kernel Sparse Coding and Dictionary Learning—0
Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models—0
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data—0
Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network—0
A Scrambled Method of Moments—0
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation—0
Gait complexity assessed by detrended fluctuation analysis is sensitive to inconsistencies in stride time series: A modeling study—0
GAGE: Geometry Preserving Attributed Graph Embeddings—0
GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation—0
Confidence-Guided Learning Process for Continuous Classification of Time Series—0
GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series—0
Gaussian mixture model decomposition of multivariate signals—0
Finite volume method network for acceleration of unsteady computational fluid dynamics: non-reacting and reacting flows—0
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Fuzzy Longest Common Subsequence Matching With FCM Using R—0
Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty—0
Gaussian variational approximation with sparse precision matrices—0
G-CMP: Graph-enhanced Contextual Matrix Profile for unsupervised anomaly detection in sensor-based remote health monitoring—0
GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network—0
GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU—0
ASAT: Adaptively Scaled Adversarial Training in Time Series—0
GenAD: General Representations of Multivariate Time Series for Anomaly Detection—0
A Data-Driven Method for Recognizing Automated Negotiation Strategies—0
A Cellular Automaton Model for the generation of Brainwaves—0
Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms—0
Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of Efficiency within the Confines of the Time Series Classification Task—0
Generalisation in fully-connected neural networks for time series forecasting—0
Conditional Loss and Deep Euler Scheme for Time Series Generation—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation—0
Generalizable autoregressive modeling of time series through functional narratives—0
Fusing Continuous-valued Medical Labels using a Bayesian Model—0
Generalization of Auto-Regressive Hidden Markov Models to Non-Linear Dynamics and Unit Quaternion Observation Space—0
Fused Lasso for Feature Selection using Structural Information—0
Artificial neural network as a universal model of nonlinear dynamical systems—0
Among-site variability in the stochastic dynamics of East African coral reefs—0
Detection and Forecasting of Extreme event in Stock Price Triggered by Fundamental, Technical, and External Factors—0
Functional Time Series Forecasting: Functional Singular Spectrum Analysis Approaches—0
Conditional Risk Minimization for Stochastic Processes—0
Functional Mixtures-of-Experts—0
Generating an Explainable ECG Beat Space With Variational Auto-Encoders—0
Functional Connectivity Dynamics show Resting-State Instability and Rightward Parietal Dysfunction in ADHD—0
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting—0
Generating Long-term Continuous Multi-type Generation Profiles—0
Kernel Hypothesis Testing with Set-valued Data—0
Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder—0
Functional Classwise Principal Component Analysis: A Novel Classification Framework—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