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 4801–4825 of 6748 papers

TitleStatusHype
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta LearningCode0
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural NetworkCode0
Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques—0
Deep Generative Quantile-Copula Models for Probabilistic Forecasting—0
Recurrent Neural Networks: An Embedded Computing Perspective—0
Time Series Analysis of Electricity Price and Demand to Find Cyber-attacks using Stationary Analysis—0
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices—0
Trading via Image ClassificationCode0
TSRuleGrowth : Extraction de règles de prédiction semi-ordonnées à partir d'une série temporelle d'éléments discrets, application dans un contexte d'intelligence ambiante—0
Classification with the matrix-variate-t distribution—0
Failure Analysis on Multivariate Time-series Data given Uncertain Labels—0
Analysis and development of an automatic eCall for motorcycles: a one-class cepstrum approach—0
Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample DatasetsCode0
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIICode0
A hybrid method of Exponential Smoothing and Recurrent Neural Networks for time series forecastingCode0
Convolutional Reservoir Computing for World ModelsCode0
An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series—0
Can Machine Learning Identify Governing Laws For Dynamics in Complex Engineered Systems ? : A Study in Chemical Engineering—0
Clustering Activity-Travel Behavior Time Series using Topological Data Analysis—0
Remaining Useful Lifetime Prediction via Deep Domain Adaptation—0
Meta-descent for Online, Continual Prediction—0
End-To-End Prediction of Emotion From Heartbeat Data Collected by a Consumer Fitness Tracker—0
Quant GANs: Deep Generation of Financial Time Series—0
Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks—0
Dynamical Systems as Temporal Feature Spaces—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