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 6276–6300 of 6748 papers

TitleStatusHype
Machine Learning Algorithms to Assess Site Closure Time Frames for Soil and Groundwater ContaminationCode0
Unsupervised Scalable Representation Learning for Multivariate Time SeriesCode0
Auxiliary Quantile Forecasting with Linear NetworksCode0
An attention model to analyse the risk of agitation and urinary tract infections in people with dementiaCode0
A Recurrent Neural Network Survival Model: Predicting Web User Return TimeCode0
Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series PredictionCode0
Deep Particulate Matter Forecasting Model Using Correntropy-Induced LossCode0
Phase Harmonic Correlations and Convolutional Neural NetworksCode0
Anamnesic Neural Differential Equations with Orthogonal Polynomial ProjectionsCode0
Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning ApproachCode0
Fitting stochastic predator-prey models using both population density and kill rate dataCode0
Adaptive pooling operators for weakly labeled sound event detectionCode0
Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time SeriesCode0
Time Series Analysis of Blockchain-Based Cryptocurrency Price ChangesCode0
Machine Learning for Neuroimaging with Scikit-LearnCode0
Analyzing Linear Dynamical Systems: From Modeling to Coding and LearningCode0
Uncertainty-DTW for Time Series and SequencesCode0
Conditional Time Series Forecasting with Convolutional Neural NetworksCode0
Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series ModelingCode0
Towards Neural Numeric-To-Text Generation From Temporal Personal Health DataCode0
Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic TomographyCode0
Support Vector Machines with Time Series Distance Kernels for Action ClassificationCode0
Seglearn: A Python Package for Learning Sequences and Time SeriesCode0
Accelerating Neural Architecture Search using Performance PredictionCode0
Machine learning modeling for time series problem: Predicting flight ticket pricesCode0
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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