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 1501–1550 of 6748 papers

TitleStatusHype
Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?Code0
Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation—0
The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator—0
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series ForecastCode0
An Extensive Data Processing Pipeline for MIMIC-IVCode2
Sparse-Group Log-Sum Penalized Graphical Model Learning For Time Series—0
Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell GenomicsCode0
CATNet: Cross-event Attention-based Time-aware Network for Medical Event Prediction—0
Digital Twin Framework for Time to Failure Forecasting of Wind Turbine Gearbox: A Concept—0
Transformers in Time-series Analysis: A Tutorial—0
COSTI: a New Classifier for Sequences of Temporal IntervalsCode0
Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version—0
Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction—0
Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of Efficiency within the Confines of the Time Series Classification Task—0
Topological Signal Processing using the Weighted Ordinal Partition Network—0
Open challenges for Machine Learning based Early Decision-Making researchCode1
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification—0
Modeling dynamic volatility under uncertain environment with fuzziness and randomness—0
Correcting motion induced fluorescence artifacts in two-channel neural imaging—0
Forecasting foreign exchange rates with regression networks tuned by Bayesian optimization—0
Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural NetworkCode1
Double Diffusion Maps and their Latent Harmonics for Scientific Computations in Latent Space—0
Time Series Prediction by Multi-task GPR with Spatiotemporal Information TransformationCode0
GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU—0
Data-driven prediction and control of extreme events in a chaotic flow—0
Quantum Bohmian Inspired Potential to Model Non-Gaussian Events and the Application in Financial Markets—0
Learning to Attack Powergrids with DERs—0
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping—0
Satellite Image Time Series Analysis for Big Earth Observation DataCode2
Multi-sensor Suboptimal Fusion Student's t Filter—0
AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphsCode0
Dimension Reduction for time series with Variational AutoEncoders—0
On the semantics of big Earth observation data for land classification—0
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles—0
Time Series Forecasting (TSF) Using Various Deep Learning Models—0
Causal Analysis of Generic Time Series Data Applied for Market Prediction—0
NLP Based Anomaly Detection for Categorical Time Series—0
Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPsCode0
Learning Sequential Latent Variable Models from Multimodal Time Series DataCode0
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting—0
STD: A Seasonal-Trend-Dispersion Decomposition of Time SeriesCode1
From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecastingCode1
A data filling methodology for time series based on CNN and (Bi)LSTM neural networks—0
Per-run Algorithm Selection with Warm-starting using Trajectory-based Features—0
Scale Dependencies and Self-Similar Models with Wavelet Scattering SpectraCode1
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential EquationsCode0
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting—0
Sintel: A Machine Learning Framework to Extract Insights from SignalsCode3
A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization—0
An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions—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