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 6076–6100 of 6748 papers

TitleStatusHype
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 2 Validation—0
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 1 Theory—0
Deep Learning for Time-Series Analysis—0
Detecting changes in slope with an L_0 penalty—0
Evaluating Preprocessing Strategies for Time Series Prediction Using Deep Learning Architectures—0
Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals—0
Information theoretical study of cross-talk mediated signal transduction in MAPK pathways—0
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics—0
Inference of Causal Information Flow in Collective Animal Behavior—0
A Basic Recurrent Neural Network Model—0
A Method for Massively Parallel Analysis of Time Series—0
Robust Online Time Series Prediction with Recurrent Neural Networks—0
Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains—0
Unsupervised Learning for Computational PhenotypingCode0
A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements—0
Probabilistic graphical model based approach for water mapping using GaoFen-2 (GF-2) high resolution imagery and Landsat 8 time series—0
Logic-based Clustering and Learning for Time-Series Data—0
Temporal Feature Selection on Networked Time Series—0
Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model—0
Mixing Times and Structural Inference for Bernoulli Autoregressive Processes—0
Automatic time-series phenotyping using massive feature extraction—0
Learning binary or real-valued time-series via spike-timing dependent plasticity—0
Graphical RNN Models—0
Fast Stability Scanning for Future Grid Scenario Analysis—0
A probabilistic graphical model approach in 30 m land cover mapping with multiple data sources—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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