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 5526–5550 of 6748 papers

TitleStatusHype
Structured Bayesian Gaussian process latent variable model—0
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive LearningCode0
NEWMA: a new method for scalable model-free online change-point detectionCode0
STS Classification with Dual-stream CNN—0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
On Attention Models for Human Activity Recognition—0
Deep Generative Markov State ModelsCode0
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference—0
Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy—0
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions—0
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives—0
Improved Predictive Models for Acute Kidney Injury with IDEAs: Intraoperative Data Embedded Analytics—0
Robust and Scalable Models of Microbiome Dynamics—0
Structural Breaks in Time Series—0
Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective—0
Towards a universal neural network encoder for time series—0
An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending—0
Foundations of Sequence-to-Sequence Modeling for Time Series—0
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders—0
Real-time regression analysis with deep convolutional neural networksCode0
30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine—0
Dynamic and Static Topic Model for Analyzing Time-Series Document Collections—0
Modeling Dengue Vector Population Using Remotely Sensed Data and Machine Learning—0
Using Quantum Mechanics to Cluster Time Series—0
An Evaluation of Classification and Outlier Detection Algorithms—0
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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