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 451–475 of 6748 papers

TitleStatusHype
Estimating Treatment Effects in Continuous Time with Hidden Confounders—0
Redes Generativas Adversarias (GAN) Fundamentos Teóricos y Aplicaciones—0
FrAug: Frequency Domain Augmentation for Time Series ForecastingCode1
Forecasting with Deep LearningCode1
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series DataCode1
JANA: Jointly Amortized Neural Approximation of Complex Bayesian ModelsCode2
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data—0
Graphical estimation of multivariate count time series—0
A Transformer-based Deep Learning Algorithm to Auto-record Undocumented Clinical One-Lung Ventilation Events—0
PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech EnhancementCode1
A Neural PDE Solver with Temporal Stencil ModelingCode1
Temporal Graph Neural Networks for Irregular DataCode1
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models—0
Functional Connectivity Dynamics show Resting-State Instability and Rightward Parietal Dysfunction in ADHD—0
Improved Online Conformal Prediction via Strongly Adaptive Online LearningCode1
CUTS: Neural Causal Discovery from Irregular Time-Series DataCode1
Excess risk bound for deep learning under weak dependence—0
Online Detection of Changes in Moment-Based Projections: When to Retrain Deep Learners or Update Portfolios?—0
Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand—0
Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes—0
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning InfusionCode1
Continuous-time convolutions model of event sequencesCode0
Fourier-RNNs for Modelling Noisy Physics Data—0
Label-efficient Time Series Representation Learning: A Review—0
One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular DataCode1
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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