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 876–900 of 6748 papers

TitleStatusHype
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior—0
Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram—0
Multimodal Neural Network For Demand Forecasting—0
Theoretical analysis of deep neural networks for temporally dependent observations—0
Neural ODEs as Feedback Policies for Nonlinear Optimal ControlCode1
Anytime-valid off-policy inference for contextual banditsCode1
Irregularly-Sampled Time Series Modeling with Spline Networks—0
Improving Medical Predictions by Irregular Multimodal Electronic Health Records ModelingCode1
Universal hidden monotonic trend estimation with contrastive learning—0
Soil moisture estimation from Sentinel-1 interferometric observations over arid regionsCode1
Research of an optimization model for servicing a network of ATMs and information payment terminals—0
Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability—0
Optimal Event Monitoring through Internet Mashup over Multivariate Time Series—0
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency AnalysisCode1
Modelling Emotion Dynamics in Song Lyrics with State Space Models—0
tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving GraphsCode0
Temporal-Spatial dependencies ENhanced deep learning model (TSEN) for household leverage series forecasting—0
Dynamic Topological Data Analysis of Functional Human Brain NetworksCode1
Flipped Classroom: Effective Teaching for Time Series ForecastingCode0
From time-series transcriptomics to gene regulatory networks: a review on inference methods—0
Extreme-Long-short Term Memory for Time-series Prediction—0
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm—0
Bandwidth-efficient distributed neural network architectures with application to body sensor networks—0
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems—0
An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different DimensionsCode0
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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