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 2426–2450 of 6748 papers

TitleStatusHype
An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis—0
ADSaS: Comprehensive Real-time Anomaly Detection System—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
Deep Bayesian Nonparametric Tracking—0
Deep Baseline Network for Time Series Modeling and Anomaly Detection—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation—0
Attentive Gaussian processes for probabilistic time-series generation—0
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach—0
An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series—0
Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations—0
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
Attention Mechanism for Multivariate Time Series Recurrent Model Interpretability Applied to the Ironmaking Industry—0
DeepAISE -- An End-to-End Development and Deployment of a Recurrent Neural Survival Model for Early Prediction of Sepsis—0
Deep Air Quality Forecasting Using Hybrid Deep Learning Framework—0
Attention Is Not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion—0
An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics—0
AttentionFlow: Visualising Influence in Networks of Time Series—0
Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition—0
Attention-based Neural Network for Driving Environment Complexity Perception—0
An Auto-Regressive Formulation for Smoothing and Moving Mean with Exponentially Tapered Windows—0
Admissible Time Series Motif Discovery with Missing Data—0
A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes—0
A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction—0
Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector—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