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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 5351–5400 of 6748 papers

TitleStatusHype
Nonlinear Evolution via Spatially-Dependent Linear Dynamics for Electrophysiology and Calcium Data—0
Towards a Near Universal Time Series Data Mining Tool: Introducing the Matrix Profile—0
Representation Learning by Reconstructing Neighborhoods—0
Theoretical and Experimental Analysis on the Generalizability of Distribution Regression Network—0
Challenges in detecting evolutionary forces in language change using diachronic corporaCode0
Data-driven Perception of Neuron Point Process with Unknown UnknownsCode0
Can automated smoothing significantly improve benchmark time series classification algorithms?—0
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction—0
PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion—0
Representation of Word Meaning in the Intermediate Projection Layer of a Neural Language Model—0
Contrastive Multivariate Singular Spectrum Analysis—0
Targeted stochastic gradient Markov chain Monte Carlo for hidden Markov models with rare latent statesCode0
The UEA multivariate time series classification archive, 2018Code0
Adaptive Extreme Learning Machine for Recurrent Beta-basis Function Neural Network Training—0
Phase Harmonic Correlations and Convolutional Neural NetworksCode0
Semi-unsupervised Learning of Human Activity using Deep Generative ModelsCode0
Bayesian nonparametric sparse VAR models—0
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality—0
Time series clustering based on the characterisation of segment typologies—0
The CoSTAR Block Stacking Dataset: Learning with Workspace ConstraintsCode0
Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals—0
Deep Poisson gamma dynamical systems—0
DeepDPM: Dynamic Population Mapping via Deep Neural Network—0
Deep Learning with Long Short-Term Memory for Time Series Prediction—0
Precipitation Nowcasting: Leveraging bidirectional LSTM and 1D CNN—0
Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders—0
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
Using Deep Learning for price prediction by exploiting stationary limit order book features—0
Stochastic Gradient MCMC for State Space ModelsCode0
Deep multi-survey classification of variable stars—0
Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing—0
Nonlinear methods to quantify Movement Variability in Human-Humanoid Interaction ActivitiesCode0
A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments—0
The UCR Time Series ArchiveCode0
Deep Diabetologist: Learning to Prescribe Hyperglycemia Medications with Hierarchical Recurrent Neural Networks—0
Study of non-linear viscoelastic behavior of the human red blood cell—0
Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences—0
Comparing Temporal Graphs Using Dynamic Time WarpingCode0
Specific low frequency electromagnetic fields induce epigenetic and functional changes in U937 cells—0
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach—0
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras—0
Spikebench: An open benchmark for spike train time-series classificationCode0
Mining Novel Multivariate Relationships in Time Series Data Using Correlation NetworksCode0
Discretizing Logged Interaction Data Biases Learning for Decision-Making—0
Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation—0
Robust multivariate and functional archetypal analysis with application to financial time series analysis—0
Learning Deep Representations from Clinical Data for Chronic Kidney Disease—0
Caulking the Leakage Effect in MEEG Source Connectivity AnalysisCode0
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces—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