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 4151–4175 of 6748 papers

TitleStatusHype
Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis—0
Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling—0
User Engagement in Mobile Health Applications—0
U-shaped Transformer: Retain High Frequency Context in Time Series Analysis—0
Using an Ancillary Neural Network to Capture Weekends and Holidays in an Adjoint Neural Network Architecture for Intelligent Building Management—0
Using Autoencoders To Learn Interesting Features For Detecting Surveillance Aircraft—0
Using Bayesian Dynamical Systems for Motion Template Libraries—0
Using Clinical Notes for ICU Management—0
Using CNNs for AD classification based on spatial correlation of BOLD signals during the observation—0
Using Conceptual Spaces to Model Domain Knowledge in Data-to-Text Systems—0
Using Connectome Features to Constrain Echo State Networks—0
Using Deep Learning for price prediction by exploiting stationary limit order book features—0
Using Deep Learning to Correlate Reddit Posts with Economic Time Series During the COVID-19 Pandemic—0
Using deep neural networks to improve the precision of fast-sampled particle timing detectors—0
Using Detailed Access Trajectories for Learning Behavior Analysis—0
Using Entropy Measures for Monitoring the Evolution of Activity Patterns—0
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets—0
Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems—0
Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links—0
Using Machine Learning to Predict Realized Variance—0
Using Neural Networks by Modelling Semi-Active Shock Absorber—0
Using Quantum Mechanics to Cluster Time Series—0
Using Spatio-temporal Deep Learning for Forecasting Demand and Supply-demand Gap in Ride-hailing System with Anonymised Spatial Adjacency Information—0
Using Synthetic Data to Improve the Long-range Forecasting of Time Series Data—0
Using Temporal and Topological Features for Intrusion Detection in Operational Networks—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