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 776–800 of 6748 papers

TitleStatusHype
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