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 5051–5100 of 6748 papers

TitleStatusHype
Network Intrusion Detection based on LSTM and Feature Embedding—0
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data—0
Neural Additive Models for Nowcasting—0
Neural Architecture Searching for Facial Attributes-based Depression Recognition—0
Neural Causal Discovery with Learnable Input Noise—0
Neural CDEs for Long Time Series via the Log-ODE Method—0
Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks—0
Neural Continuous-Time Markov Models—0
Neural Decomposition of Time-Series Data for Effective Generalization—0
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics—0
Neural Echo State Network using oscillations of gas bubbles in water—0
Neural forecasting at scale—0
Neural Generalised AutoRegressive Conditional Heteroskedasticity—0
Neural Learning of Online Consumer Credit Risk—0
Neural Machine Translation for Fact-checking Temporal Claims—0
Neural Markov Controlled SDE: Stochastic Optimization for Continuous-Time Data—0
Neural Multi-Quantile Forecasting for Optimal Inventory Management—0
Neural Network-based Automatic Factor Construction—0
Neural Network for Weighted Signal Temporal Logic—0
Neural ODEs for Informative Missingness in Multivariate Time Series—0
Neural ODE to model and prognose thermoacoustic instability—0
Neural Ordinary Differential Equation based Recurrent Neural Network Model—0
Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications—0
Neural Spline Search for Quantile Probabilistic Modeling—0
Neural Time Warping For Multiple Sequence Alignment—0
Neural Topic Modeling of Psychotherapy Sessions—0
Neuro Fuzzy Modelling for Prediction of Consumer Price Index—0
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge—0
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description—0
NeuroView-RNN: It's About Time—0
New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence—0
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data—0
News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions—0
Nextplace: a spatio-temporal prediction framework for pervasive systems—0
NLP Based Anomaly Detection for Categorical Time Series—0
Noise-cleaning the precision matrix of fMRI time series—0
Non-contrastive representation learning for intervals from well logs—0
Non Intrusive Load Monitoring in Chaotic Switching Networks—0
Non-intrusive Water Usage Classification Considering Limited Training Data—0
Nonlinear Deconvolution by Sampling Biophysically Plausible Hemodynamic Models—0
Nonlinear Dimensionality Reduction on Graphs—0
Nonlinearity in stock networks—0
Nonlinear least-squares spline fitting with variable knots—0
Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity—0
Non-Linear Temporal Subspace Representations for Activity Recognition—0
Nonlinear Time Series Modeling: A Unified Perspective, Algorithm, and Application—0
Nonlinear trend removal should be carefully performed in heart rate variability analysis—0
Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates—0
Nonnegative matrix factorization with side information for time series recovery and prediction—0
Nonparametric Analysis of Dynamic Random Utility Models—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