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

Showing 251300 of 6748 papers

TitleStatusHype
Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series AnalysisCode1
High-Dimensional Granger Causality Tests with an Application to VIX and NewsCode1
Cost-effective Interactive Attention Learning with Neural Attention ProcessesCode1
Euler State Networks: Non-dissipative Reservoir ComputingCode1
Contrastive Domain Adaptation for Time-Series via Temporal MixupCode1
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact DataCode1
Copula Conformal Prediction for Multi-step Time Series ForecastingCode1
Convolution-enhanced Evolving Attention NetworksCode1
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal BootstrappingCode1
Convolutional Radio Modulation Recognition NetworksCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
Random Dilated Shapelet Transform: A New Approach for Time Series ShapeletsCode1
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential EquationsCode1
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
Continuous Latent Process FlowsCode1
Continual Transformers: Redundancy-Free Attention for Online InferenceCode1
Continuous-Time Deep Glioma Growth ModelsCode1
Neural graphical modelling in continuous-time: consistency guarantees and algorithmsCode1
AA-Forecast: Anomaly-Aware Forecast for Extreme EventsCode1
Construe: a software solution for the explanation-based interpretation of time seriesCode1
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential EquationsCode1
Conformal prediction set for time-seriesCode1
Conformal Time-series ForecastingCode1
Conditional GAN for timeseries generationCode1
Conditional Sig-Wasserstein GANs for Time Series GenerationCode1
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural NetworksCode1
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-SeriesCode1
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Color-aware two-branch DCNN for efficient plant disease classificationCode1
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task LearningCode1
Closed-Form Diffeomorphic Transformations for Time Series AlignmentCode1
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
Classification of Long Sequential Data using Circular Dilated Convolutional Neural NetworksCode1
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image RepresentationCode1
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time WarpingCode1
DeepMoD: Deep learning for Model Discovery in noisy dataCode1
ClaSP - Time Series SegmentationCode1
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
The Signature Kernel is the solution of a Goursat PDECode1
Contrastive Learning for Unsupervised Domain Adaptation of Time SeriesCode1
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse EffectsCode1
Data-driven discovery of intrinsic dynamicsCode1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
Changing Fashion CulturesCode1
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
catch22: CAnonical Time-series CHaracteristicsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified