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 4851–4875 of 6748 papers

TitleStatusHype
A semi-supervised autoencoder framework for joint generation and classification of breathing—0
MTHetGNN: A Heterogeneous Graph Embedding Framework for Multivariate Time Series Forecasting—0
Modeling Deep Temporal Dependencies with Recurrent Grammar Cells""—0
Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor—0
Modeling Dengue Vector Population Using Remotely Sensed Data and Machine Learning—0
Modeling dynamic volatility under uncertain environment with fuzziness and randomness—0
Modeling Evolution of Message Interaction for Rumor Resolution—0
Modeling Extreme Events in Time Series Prediction—0
Modeling joint probability distribution of yield curve parameters—0
Unleashing the Power of Shared Label Structures for Human Activity Recognition—0
Modeling Missing Data in Clinical Time Series with RNNs—0
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies—0
Modeling non-stationarities in high-frequency financial time series—0
Modeling of Low Rank Time Series—0
Modeling of Political Systems using Wasserstein Gradient Flows—0
Modeling of time series using random forests: theoretical developments—0
Modeling Polyp Activity of Paragorgia arborea Using Supervised Learning—0
Modeling Randomly Walking Volatility with Chained Gamma Distributions—0
Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units—0
Modeling Regime Shifts in Multiple Time Series—0
Modeling Sub-Event Dynamics in First-Person Action Recognition—0
Modeling Systems with Machine Learning based Differential Equations—0
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion—0
Modeling the Chlorophyll-a from Sea Surface Reflectance in West Africa by Deep Learning Methods: A Comparison of Multiple Algorithms—0
Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding—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