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 1501–1525 of 6748 papers

TitleStatusHype
Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?Code0
The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator—0
Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation—0
An Extensive Data Processing Pipeline for MIMIC-IVCode2
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series ForecastCode0
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
Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of Efficiency within the Confines of the Time Series Classification Task—0
Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction—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
Topological Signal Processing using the Weighted Ordinal Partition Network—0
Modeling dynamic volatility under uncertain environment with fuzziness and randomness—0
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification—0
Open challenges for Machine Learning based Early Decision-Making researchCode1
Correcting motion induced fluorescence artifacts in two-channel neural imaging—0
Time Series Prediction by Multi-task GPR with Spatiotemporal Information TransformationCode0
Forecasting foreign exchange rates with regression networks tuned by Bayesian optimization—0
Double Diffusion Maps and their Latent Harmonics for Scientific Computations in Latent Space—0
Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural NetworkCode1
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
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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