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
Accelerating Neural ODEs Using Model Order Reduction—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
Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback—0
Configuration and Collection Factors for Side-Channel Disassembly—0
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles—0
Gaussian mixture model decomposition of multivariate signals—0
Enhance the performance of navigation: A two-stage machine learning approach—0
Enhancement of Healthcare Data Performance Metrics using Neural Network Machine Learning Algorithms—0
Gaussian process imputation of multiple financial series—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Causal Inference on Time Series using Restricted Structural Equation Models—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
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
GenAD: General Representations of Multivariate Time Series for Anomaly Detection—0
A Self-Supervised Framework for Function Learning and Extrapolation—0
AMP: a new time-frequency feature extraction method for intermittent time-series data—0
Enhanced Cyber-Physical Security through Deep Learning Techniques—0
General Hannan and Quinn Criterion for Common Time Series—0
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality—0
A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network—0
Bayesian Neural Networks for Macroeconomic Analysis—0
Generalised learning of time-series: Ornstein-Uhlenbeck processes—0
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning—0
Generalizable autoregressive modeling of time series through functional narratives—0
Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction—0
Generalization of Auto-Regressive Hidden Markov Models to Non-Linear Dynamics and Unit Quaternion Observation Space—0
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals—0
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks—0
Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models—0
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—0
A novel health risk model based on intraday physical activity time series collected by smartphones—0
Linear-time inference for Gaussian Processes on one dimension—0
General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data—0
Generating an Explainable ECG Beat Space With Variational Auto-Encoders—0
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection—0
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints—0
Generating Long-term Continuous Multi-type Generation Profiles—0
Constraints on parameter choices for successful reservoir computing—0
Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder—0
A High GOPs/Slice Time Series Classifier for Portable and Embedded Biomedical Applications—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