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 5101–5125 of 6748 papers

TitleStatusHype
Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations—0
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals—0
Nonparametric Bayesian Sparse Graph Linear Dynamical Systems—0
Nonparametric Expected Shortfall Forecasting Incorporating Weighted Quantiles—0
Nonparametric Extrema Analysis in Time Series for Envelope Extraction, Peak Detection and Clustering—0
Nonparametric Independent Component Analysis for the Sources with Mixed Spectra—0
Nonparametric risk bounds for time-series forecasting—0
Non-parametric Sparse Additive Auto-regressive Network Models—0
Nonparametric Test for Volatility in Clustered Multiple Time Series—0
Nonparametric Tests of Conditional Independence for Time Series—0
Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces—0
Non-stationary continuous dynamic Bayesian networks—0
Non-stationary dynamic Bayesian networks—0
Non-stationary Online Regression—0
NonSTOP: A NonSTationary Online Prediction Method for Time Series—0
Non-technical Loss Detection with Statistical Profile Images Based on Semi-supervised Learning—0
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks—0
Normalisation of Weights and Firing Rates in Spiking Neural Networks with Spike-Timing-Dependent Plasticity—0
Normalized multivariate time series causality analysis and causal graph reconstruction—0
Normalizing flows for novelty detection in industrial time series data—0
Normalizing Kalman Filters for Multivariate Time Series Analysis—0
Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models—0
Noumenal Labs White Paper: How To Build A Brain—0
Novel Fourier Quadrature Transforms and Analytic Signal Representations for Nonlinear and Non-stationary Time Series Analysis—0
Long Short-Term Memory with Gate and State Level Fusion for Light Field-Based Face Recognition—0
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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