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 1251–1275 of 6748 papers

TitleStatusHype
Enhancing Energy System Models Using Better Load Forecasts—0
Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data—0
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification—0
Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions—0
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting—0
Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early ClassificationCode0
CNTS: Cooperative Network for Time SeriesCode0
Ergodic characterization of non-ergodic anomalous diffusion processes—0
Estimating Treatment Effects in Continuous Time with Hidden Confounders—0
Redes Generativas Adversarias (GAN) Fundamentos Teóricos y Aplicaciones—0
Graphical estimation of multivariate count time series—0
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data—0
A Transformer-based Deep Learning Algorithm to Auto-record Undocumented Clinical One-Lung Ventilation Events—0
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models—0
Excess risk bound for deep learning under weak dependence—0
Functional Connectivity Dynamics show Resting-State Instability and Rightward Parietal Dysfunction in ADHD—0
Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand—0
Online Detection of Changes in Moment-Based Projections: When to Retrain Deep Learners or Update Portfolios?—0
Fourier-RNNs for Modelling Noisy Physics Data—0
Continuous-time convolutions model of event sequencesCode0
Label-efficient Time Series Representation Learning: A Review—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
Forecasting the Turkish Lira Exchange Rates through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?—0
Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape—0
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies—0
Show:102550
← PrevPage 51 of 270Next →
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