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 5976–6000 of 6748 papers

TitleStatusHype
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 classifierF187.47—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