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 4851–4900 of 6748 papers

TitleStatusHype
A Comparative Study of the Clinical use of Motion Analysis from Kinect Skeleton Data—0
A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
A comparison among some Hurst exponent approaches to predict nascent bubbles in 500 company stocks—0
A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series—0
A Comparison of Model-Free and Model Predictive Control for Price Responsive Water Heaters—0
A Comparison of Nineteen Various Electricity Consumption Forecasting Approaches and Practicing to Five Different Households in Turkey—0
A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders—0
A Composite Quantile Fourier Neural Network for Multi-Step Probabilistic Forecasting of Nonstationary Univariate Time Series—0
A Comprehensive Study on Various Statistical Techniques for Prediction of Movie Success—0
A conditional likelihood is required to estimate the selection coefficient in ancient DNA—0
A consistent deterministic regression tree for non-parametric prediction of time series—0
Independence Testing for Temporal Data—0
A Consistent Method for Learning OOMs from Asymptotically Stationary Time Series Data Containing Missing Values—0
A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting—0
A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization—0
A Convolutional Neural Network Approach to Supernova Time-Series Classification—0
Actionable Interpretation of Machine Learning Models for Sequential Data: Dementia-related Agitation Use Case—0
Action-Affect Classification and Morphing using Multi-Task Representation Learning—0
Action Prediction in Humans and Robots—0
Actions Generation from Captions—0
Active Privacy-Utility Trade-off Against Inference in Time-Series Data Sharing—0
Active queue management: First steps toward a new control-theoretic viewpoint—0
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data—0
Active Tuning—0
Activity recognition using ST-GCN with 3D motion data—0
Actor-Critic Approach for Temporal Predictive Clustering—0
ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence—0
A Daily Tourism Demand Prediction Framework Based on Multi-head Attention CNN: The Case of The Foreign Entrant in South Korea—0
Adaptable Hamiltonian neural networks—0
Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method—0
Adaptive Bayesian Sum of Trees Model for Covariate Dependent Spectral Analysis—0
Adaptive Complementary Ensemble EMD and Energy-Frequency Spectra of Cryptocurrency Prices—0
Adaptive deep learning for nonlinear time series models—0
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy—0
Adaptive Estimation of Graphical Models under Total Positivity—0
Adaptive exponential power distribution with moving estimator for nonstationary time series—0
Adaptive Extreme Learning Machine for Recurrent Beta-basis Function Neural Network Training—0
Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model—0
Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction—0
Adaptive hedging horizon and hedging performance estimation—0
Adaptive Inducing Points Selection For Gaussian Processes—0
Adaptive Kernel Estimation of the Spectral Density with Boundary Kernel Analysis—0
Adaptive Learning Method of Recurrent Temporal Deep Belief Network to Analyze Time Series Data—0
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift—0
Adaptive Sequential Design for a Single Time-Series—0
Adaptive Transfer Learning of Multi-View Time Series Classification—0
Adaptive Transmit Waveform Design using Multi-Tone Sinusoidal Frequency Modulation—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