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 2876–2900 of 6748 papers

TitleStatusHype
Deep Sequence Modeling: Development and Applications in Asset Pricing—0
ASAT: Adaptively Scaled Adversarial Training in Time Series—0
Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves—0
Transformers predicting the future. Applying attention in next-frame and time series forecastingCode0
Construction Cost Index Forecasting: A Multi-feature Fusion Approach—0
Federated Variational Learning for Anomaly Detection in Multivariate Time Series—0
XAI Methods for Neural Time Series Classification: A Brief Review—0
Stack Index Prediction Using Time-Series Analysis—0
Structure Parameter Optimized Kernel Based Online Prediction with a Generalized Optimization Strategy for Nonstationary Time Series—0
A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders—0
Time delay estimation of traffic congestion propagation due to accidents based on statistical causalityCode0
Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection—0
Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication—0
IT2CFNN: An Interval Type-2 Correlation-Aware Fuzzy Neural Network to Construct Non-Separable Fuzzy Rules with Uncertain and Adaptive Shapes for Nonlinear Function Approximation—0
Empirical Risk Minimization for Time Series: Nonparametric Performance Bounds for Prediction—0
AdaRNN: Adaptive Learning and Forecasting of Time SeriesCode0
Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable DataCode0
#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs—0
Ensemble neuroevolution based approach for multivariate time series anomaly detection—0
Active Learning of Driving Scenario Trajectories—0
Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computing—0
Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Series—0
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration DataCode0
Local Exceptionality Detection in Time Series Using Subgroup Discovery—0
A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse—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