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 751–800 of 6748 papers

TitleStatusHype
Distribution estimation and change-point estimation for time series via DNN-based GANs—0
EasyMLServe: Easy Deployment of REST Machine Learning ServicesCode0
EDGAR: Embedded Detection of Gunshots by AI in Real-time—0
Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network—0
Machine Learning Algorithms for Time Series Analysis and Forecasting—0
Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments—0
Leverage, Endogenous Unbalanced Growth, and Asset Price Bubbles—0
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive ModelsCode1
Time Series Forecasting with Hypernetworks Generating Parameters in Advance—0
MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series—0
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box AttackCode1
Deep learning delay coordinate dynamics for chaotic attractors from partial observable data—0
Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers—0
Finding active galactic nuclei through FinkCode1
Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods—0
Autoregressive GNN-ODE GRU Model for Network Dynamics—0
Class-Specific Attention (CSA) for Time-Series Classification—0
Step Counting with Attention-based LSTMCode0
Identifying Unique Causal Network from Nonstationary Time SeriesCode0
Fractional integration and cointegration—0
A Transformer Framework for Data Fusion and Multi-Task Learning in Smart CitiesCode0
Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic—0
DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift—0
Neural Inference of Gaussian Processes for Time Series Data of QuasarsCode0
Cointegration with Occasionally Binding Constraints—0
Parameterization of state duration in Hidden semi-Markov Models: an application in electrocardiography—0
Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM—0
Hierarchical Estimation for Effective and Efficient Sampling Graph Neural Network—0
Graph Filters for Signal Processing and Machine Learning on Graphs—0
SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series ForecastingCode1
Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP)—0
Are we certain it's anomalous?Code1
Parameter-Covariance Maximum Likelihood Estimation—0
Backdoor Attacks on Time Series: A Generative ApproachCode1
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk PredictionCode0
Motor imagery classification using EEG spectrograms—0
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural NetworksCode1
Advancing the State-of-the-Art for ECG Analysis through Structured State Space ModelsCode1
Temporal patterns in insulin needs for Type 1 diabetesCode0
Multi-VQG: Generating Engaging Questions for Multiple ImagesCode0
Similarity-based Feature Extraction for Large-scale Sparse Traffic ForecastingCode0
HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOpsCode0
Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics—0
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of IntelligenceCode0
Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression—0
Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection—0
WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley ValuesCode1
Does Deep Learning REALLY Outperform Non-deep Machine Learning for Clinical Prediction on Physiological Time Series?—0
Investigating Enhancements to Contrastive Predictive Coding for Human Activity RecognitionCode0
Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement PredictionCode1
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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