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 5951–5975 of 6748 papers

TitleStatusHype
Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?Code0
Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series DataCode0
Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage NormalizationCode0
COVID-19 epidemiology as emergent behavior on a dynamic transmission forestCode0
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health RecordsCode0
COVID-19 and the gig economy in PolandCode0
A General Deep Learning Framework for Network Reconstruction and Dynamics LearningCode0
Covariate-guided Bayesian mixture model for multivariate time seriesCode0
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual PredictionCode0
From the logistic-sigmoid to nlogistic-sigmoid: modelling the COVID-19 pandemic growthCode0
Reusing Convolutional Activations from Frame to Frame to Speed up Training and InferenceCode0
Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential EquationsCode0
LandCoverNet: A global benchmark land cover classification training datasetCode0
A fully automated periodicity detection in time seriesCode0
Langevin-gradient parallel tempering for Bayesian neural learningCode0
Coupling Oceanic Observation Systems to Study Mesoscale Ocean DynamicsCode0
COSTI: a New Classifier for Sequences of Temporal IntervalsCode0
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainabilityCode0
Harnessing the power of Topological Data Analysis to detect change points in time seriesCode0
Large Linear Multi-output Gaussian Process LearningCode0
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal frameworkCode0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 PandemicCode0
On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman FiltersCode0
Large-Scale Characterization and Segmentation of Internet Path Delays with Infinite HMMsCode0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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