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 3651–3675 of 6748 papers

TitleStatusHype
Recent scaling properties of Bitcoin price returns—0
Learning Quantities of Interest from Dynamical Systems for Observation-Consistent Inversion—0
Demand Forecasting of Individual Probability Density Functions with Machine Learning—0
Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems—0
Learning Hidden Patterns from Patient Multivariate Time Series Data Using Convolutional Neural Networks: A Case Study of Healthcare Cost Prediction—0
Healthcare Cost Prediction: Leveraging Fine-grain Temporal Patterns—0
Identifying Grey-box Thermal Models with Bayesian Neural Networks—0
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
RF-Based Low-SNR Classification of UAVs Using Convolutional Neural Networks—0
Covid-19 impact on cryptocurrencies: evidence from a wavelet-based Hurst exponent—0
Accelerated solving of coupled, non-linear ODEs through LSTM-AI—0
Machine Learning for Temporal Data in Finance: Challenges and Opportunities—0
Applications of Deep Neural Networks with KerasCode3
Spatio-Temporal Functional Neural Networks—0
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge AggregationCode0
Symplectic Gaussian Process Regression of Hamiltonian Flow Maps—0
Factor-Driven Two-Regime RegressionCode0
Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series—0
Actionable Interpretation of Machine Learning Models for Sequential Data: Dementia-related Agitation Use Case—0
Deep Switching Auto-Regressive Factorization:Application to Time Series ForecastingCode1
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data—0
Large-scale nonlinear Granger causality: A data-driven, multivariate approach to recovering directed networks from short time-series data—0
XCM: An Explainable Convolutional Neural Network for Multivariate Time Series ClassificationCode1
Forecasting financial markets with semantic network analysis in the COVID-19 crisis—0
tsBNgen: A Python Library to Generate Time Series Data from an Arbitrary Dynamic Bayesian Network StructureCode1
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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