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 526–550 of 6748 papers

TitleStatusHype
Recurrences reveal shared causal drivers of complex time seriesCode1
LogAI: A Library for Log Analytics and IntelligenceCode2
Time Series Forecasting via Semi-Asymmetric Convolutional Architecture with Global Atrous Sliding Window—0
Wavelet Analysis for Time Series Financial Signals via Element Analysis—0
Benchmarking optimality of time series classification methods in distinguishing diffusionsCode0
Data-driven soiling detection in PV modules—0
BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series—0
Approximating DTW with a convolutional neural network on EEG data—0
Global Flood Prediction: a Multimodal Machine Learning Approach—0
Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontology—0
Multidimensional dynamic factor models—0
Localizing the Origin of Idiopathic Ventricular Arrhythmia from ECG Using an Attention-Based Recurrent Convolutional Neural Network—0
GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network—0
Optimizing a Bayesian method for estimating the Hurst exponent in behavioral sciences—0
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for MedicineCode1
PrecTime: A Deep Learning Architecture for Precise Time Series Segmentation in Industrial Manufacturing Operations—0
Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing—0
Learning the Dynamics of Sparsely Observed Interacting SystemsCode0
Machine Learning Approach and Extreme Value Theory to Correlated Stochastic Time Series with Application to Tree Ring Data—0
Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based PerspectiveCode0
Targeted Attacks on Timeseries Forecasting—0
Better than DFA? A Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences—0
Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text—0
Coincident Learning for Unsupervised Anomaly Detection—0
Causal Graph Discovery from Self and Mutually Exciting Time Series—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