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 151–200 of 6748 papers

TitleStatusHype
A Review of Graph Neural Networks and Their Applications in Power SystemsCode1
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading RulesCode1
Domain Adaptation for Time Series Forecasting via Attention SharingCode1
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series ImputationCode1
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential EquationsCode1
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact DataCode1
Crop mapping from image time series: deep learning with multi-scale label hierarchiesCode1
Counterfactual Explanations for Machine Learning on Multivariate Time Series DataCode1
Arbitrage-free neural-SDE market modelsCode1
COVID-19 Data Analysis and Forecasting: Algeria and the WorldCode1
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time SeriesCode1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome PredictionCode1
Cost-effective Interactive Attention Learning with Neural Attention ProcessesCode1
Anytime-valid off-policy inference for contextual banditsCode1
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly DetectionCode1
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal BootstrappingCode1
COT-GAN: Generating Sequential Data via Causal Optimal TransportCode1
Random Dilated Shapelet Transform: A New Approach for Time Series ShapeletsCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
Convolution-enhanced Evolving Attention NetworksCode1
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-SeriesCode1
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential EquationsCode1
Contrastive Learning for Unsupervised Domain Adaptation of Time SeriesCode1
Copula Conformal Prediction for Multi-step Time Series ForecastingCode1
Contrastive Domain Adaptation for Time-Series via Temporal MixupCode1
CUTS: Neural Causal Discovery from Irregular Time-Series DataCode1
Deep ConvLSTM with self-attention for human activity decoding using wearablesCode1
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural NetworksCode1
Conformal prediction set for time-seriesCode1
Neural graphical modelling in continuous-time: consistency guarantees and algorithmsCode1
An Experimental Review on Deep Learning Architectures for Time Series ForecastingCode1
Conditional GAN for timeseries generationCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural NetworksCode1
The Signature Kernel is the solution of a Goursat PDECode1
Conditional Sig-Wasserstein GANs for Time Series GenerationCode1
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking ApplicationsCode1
A Deep Learning Approach to Analyzing Continuous-Time SystemsCode1
Conformal Time-series ForecastingCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time seriesCode1
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent AutoencodersCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Construe: a software solution for the explanation-based interpretation of time seriesCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series ForecastingCode1
Convolutional Radio Modulation Recognition NetworksCode1
A Neural PDE Solver with Temporal Stencil ModelingCode1
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time SeriesCode1
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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