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 6601–6650 of 6748 papers

TitleStatusHype
Discriminative Functional Connectivity Measures for Brain Decoding—0
Automatic Construction and Natural-Language Description of Nonparametric Regression ModelsCode0
Human Activity Recognition using Smartphone—0
Kernel Least Mean Square with Adaptive Kernel Size—0
On change point detection using the fused lasso method—0
Increasing Server Availability for Overall System Security: A Preventive Maintenance Approach Based on Failure Prediction—0
Skill Analysis with Time Series Image Data—0
Alternating direction method of multipliers for penalized zero-variance discriminant analysis—0
An Empirical Evaluation of Similarity Measures for Time Series Classification—0
Highly comparative feature-based time-series classification—0
Multi-Step-Ahead Time Series Prediction using Multiple-Output Support Vector Regression—0
Does Restraining End Effect Matter in EMD-Based Modeling Framework for Time Series Prediction? Some Experimental Evidences—0
A Comparative Study of Reservoir Computing for Temporal Signal Processing—0
Distinguishing noise from chaos: objective versus subjective criteria using Horizontal Visibility Graph—0
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting—0
Fast nonparametric clustering of structured time-series—0
Time series forecasting using neural networks—0
PSO-MISMO Modeling Strategy for Multi-Step-Ahead Time Series Prediction—0
A regression model with a hidden logistic process for feature extraction from time series—0
Time series modeling by a regression approach based on a latent process—0
Joint segmentation of multivariate time series with hidden process regression for human activity recognition—0
Model-based clustering with Hidden Markov Model regression for time series with regime changes—0
Model-based clustering and segmentation of time series with changes in regime—0
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression—0
Invariant Factorization Of Time-Series—0
Classifiers With a Reject Option for Early Time-Series Classification—0
Particle Swarm Optimization of Information-Content Weighting of Symbolic Aggregate Approximation—0
雜訊環境下應用線性估測編碼於特徵時序列之強健性語音辨識 (Employing Linear Prediction Coding in Feature Time Sequences for Robust Speech Recognition in Noisy Environments) [In Chinese]—0
Multilinear Dynamical Systems for Tensor Time Series—0
Locally Adaptive Bayesian Multivariate Time Series—0
Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions—0
Causal Inference on Time Series using Restricted Structural Equation Models—0
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators.—0
Training and Analysing Deep Recurrent Neural Networks—0
Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series—0
Universal Codes from Switching Strategies—0
A Unified SVM Framework for Signal Estimation—0
A Visibility Graph Averaging Aggregation Operator—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Constructing Time Series Shape Association Measures: Minkowski Distance and Data Standardization—0
Joint Estimation of Multiple Graphical Models from High Dimensional Time Series—0
Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series—0
Observing Features of PTT Neologisms: A Corpus-driven Study with N-gram Model—0
Time Series Topic Modeling and Bursty Topic Detection of Correlated News and Twitter—0
How Noisy Social Media Text, How Diffrnt Social Media Sources?—0
Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package—0
On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection—0
Mixed Membership Models for Time Series—0
Temporal Autoencoding Improves Generative Models of Time Series—0
Learning from the past, predicting the statistics for the future, learning an evolving systemCode0
Show:102550
← PrevPage 133 of 135Next →
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