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 5701–5725 of 6748 papers

TitleStatusHype
TSViz: Demystification of Deep Learning Models for Time-Series AnalysisCode0
Brain-inspired photonic signal processor for periodic pattern generation and chaotic system emulation—0
Weakly-supervised Dictionary Learning—0
Lie Transform--based Neural Networks for Dynamics Simulation and Learning—0
Parsimonious Network based on Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis—0
Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain FeaturesCode0
Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures—0
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks—0
ReNN: Rule-embedded Neural Networks—0
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG IdentificationCode0
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding—0
Nonlinear Dimensionality Reduction on Graphs—0
Empirical observations of ultraslow diffusion driven by the fractional dynamics in languages: Dynamical statistical properties of word counts of already popular words—0
Spurious seasonality detection: a non-parametric test proposal—0
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data—0
Non-parametric Sparse Additive Auto-regressive Network Models—0
Characterization of catastrophic instabilities: Market crashes as paradigm—0
News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions—0
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs—0
Fractal analyses of networks of integrate-and-fire stochastic spiking neurons—0
A First Option Calibration of the GARCH Diffusion Model by a PDE Method—0
Invariants of multidimensional time series based on their iterated-integral signature—0
Large-Scale Simulation of Multi-Asset Ising Financial Markets—0
Seismic-Net: A Deep Densely Connected Neural Network to Detect Seismic Events—0
Time Series Segmentation through Automatic Feature Learning—0
Show:102550
← PrevPage 229 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