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 1026–1050 of 6748 papers

TitleStatusHype
Information Theoretic Measures of Causal Influences during Transient Neural Events—0
Improving Accuracy and Explainability of Online Handwriting RecognitionCode0
Explainable AI for clinical and remote health applications: a survey on tabular and time series data—0
Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System—0
Scalable Spatiotemporal Graph Neural NetworksCode1
Time Series Prediction for Food sustainabilityCode0
TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective AttackCode1
Bioeconomic analysis of harvesting within a predator-prey system: A case study in the Chesapeake Bay fisheries—0
Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series dataCode0
A topological analysis of cointegrated data: a Z24 Bridge case study—0
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data—0
Fairness in Forecasting of Observations of Linear Dynamical SystemsCode0
Uncovering Regions of Maximum Dissimilarity on Random Process Data—0
An Evaluation of Low Overhead Time Series Preprocessing Techniques for Downstream Machine Learning—0
A new hazard event classification model via deep learning and multifractal—0
Modeling of Political Systems using Wasserstein Gradient Flows—0
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning—0
Structured Recognition for Generative Models with Explaining AwayCode0
Testing the martingale difference hypothesis in high dimension—0
Deep Baseline Network for Time Series Modeling and Anomaly Detection—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
Symbolic Knowledge Extraction from Opaque Predictors Applied to Cosmic-Ray Data Gathered with LISA Pathfinder—0
Yes, DLGM! A novel hierarchical model for hazard classification—0
Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering AlgorithmCode1
In-situ animal behavior classification using knowledge distillation and fixed-point quantization—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