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 2851–2875 of 6748 papers

TitleStatusHype
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality—0
A Data-Driven Method for Recognizing Automated Negotiation Strategies—0
GAGE: Geometry Preserving Attributed Graph Embeddings—0
Gait complexity assessed by detrended fluctuation analysis is sensitive to inconsistencies in stride time series: A modeling study—0
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data—0
Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models—0
A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Bayesian Neural Networks for Macroeconomic Analysis—0
Configuration and Collection Factors for Side-Channel Disassembly—0
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning—0
Gaussian mixture model decomposition of multivariate signals—0
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals—0
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
Gaussian process imputation of multiple financial series—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks—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
Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models—0
GenAD: General Representations of Multivariate Time Series for Anomaly Detection—0
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—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