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 59516000 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
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class ClassificationCode0
Theory and Algorithms for Shapelet-based Multiple-Instance LearningCode0
An Image Processing approach to identify solar plages observed at 393.37 nm by the Kodaikanal Solar ObservatoryCode0
A Framework for Imbalanced Time-series ForecastingCode0
Detrended Partial-Cross-Correlation Analysis: A New Method for Analyzing Correlations in Complex SystemCode0
Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell GenomicsCode0
Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the YearsCode0
The Power of Linear Recurrent Neural NetworksCode0
Online nonnegative CP-dictionary learning for Markovian dataCode0
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy OptimizationCode0
Guidelines for Augmentation Selection in Contrastive Learning for Time Series ClassificationCode0
LARNN: Linear Attention Recurrent Neural NetworkCode0
On Regularizability and its Application to Online Control of Unstable LTI SystemsCode0
Online Search With Best-Price and Query-Based PredictionsCode0
LASSO-ODE: A framework for mechanistic model identifiability and selection in disease transmission modelingCode0
Cost-effective Interactive Attention Learning with Neural Attention ProcessCode0
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge AggregationCode0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
Generalizing to unseen domains via distribution matchingCode0
Latent Dynamic Factor Analysis of High-Dimensional Neural RecordingsCode0
Unsupervised dynamic modeling of medical image transformationCode0
R-FORCE: Robust Learning for Random Recurrent Neural NetworksCode0
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsCode0
Latent Ordinary Differential Equations for Irregularly-Sampled Time SeriesCode0
Online Topology Identification from Vector Autoregressive Time SeriesCode0
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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