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

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Showing 6051–6100 of 6748 papers

TitleStatusHype
Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis—0
Fractal approach towards power-law coherency to measure cross-correlations between time series—0
Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment—0
Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries—0
A Goal-Based Movement Model for Continuous Multi-Agent Tasks—0
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational CostsCode0
Structural Change in (Economic) Time Series—0
Detecting and modelling delayed density-dependence in abundance time series of a small mammal (Didelphis aurita)—0
Crossmatching variable objects with the Gaia data—0
Coresets for Kernel Regression—0
Similarity Preserving Representation Learning for Time Series Clustering—0
Robust Clustering for Time Series Using Spectral Densities and Functional Data Analysis—0
Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data—0
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets—0
Fast and Accurate Time Series Classification with WEASELCode0
Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case—0
Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces—0
Topology data analysis of critical transitions in financial networks—0
Integrated information and dimensionality in continuous attractor dynamics—0
Temporal Overdrive Recurrent Neural Network—0
Towards prediction of rapid intensification in tropical cyclones with recurrent neural networks—0
Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation—0
Learning Linear Dynamical Systems with High-Order Tensor Data for Skeleton based Action Recognition—0
Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review—0
See the Near Future: A Short-Term Predictive Methodology to Traffic Load in ITS—0
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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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