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 3201–3225 of 6748 papers

TitleStatusHype
Interpretable Models for Granger Causality Using Self-explaining Neural NetworksCode1
Q4EDA: A Novel Strategy for Textual Information Retrieval Based on User Interactions with Visual Representations of Time Series—0
Optimizing Hyperparameters in CNNs using Bilevel Programming in Time Series Data—0
DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural NetworksCode0
Flow Forecast: A deep learning for time series forecasting, classification, and anomaly detection framework built in PyTorch—0
Discrete Graph Structure Learning for Forecasting Multiple Time SeriesCode1
An attention model to analyse the risk of agitation and urinary tract infections in people with dementiaCode0
Online detection of failures generated by storage simulator—0
Diagnosis of systemic risk and contagion across financial sectors—0
Free congruence: an exploration of expanded similarity measures for time series data—0
Dynamical prediction of two meteorological factors using the deep neural network and the long short term memory (1)—0
Morphological Change Forecasting for Prostate Glands using Feature-based Registration and Kernel Density Extrapolation—0
A Renormalization Group Approach to Connect Discrete- and Continuous-Time Descriptions of Gaussian Processes—0
Parameter inference in a computational model of hemodynamics in pulmonary hypertension—0
Fitting very flexible models: Linear regression with large numbers of parameters—0
A Novel Cluster Classify Regress Model Predictive Controller Formulation; CCR-MPC—0
TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point ClusteringCode0
Adequacy of time-series reduction for renewable energy systems—0
A General Framework for Hypercomplex-valued Extreme Learning Machines—0
A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data—0
Physics-aware, probabilistic model order reduction with guaranteed stability—0
Unveiling the role of plasticity rules in reservoir computing—0
Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning—0
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series—0
Deep State Inference: Toward Behavioral Model Inference of Black-box Software SystemsCode0
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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