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 3726–3750 of 6748 papers

TitleStatusHype
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection—0
Evaluating data augmentation for financial time series classificationCode0
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
Augmenting transferred representations for stock classification—0
Hybrid Backpropagation Parallel Reservoir Networks—0
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent KernelsCode0
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks—0
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting—0
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review—0
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems—0
A Systematic Comparison of Forecasting for Gross Domestic Product in an Emergent Economy—0
Quasi-steady uptake and bacterial community assembly in a mathematical model of soil-phosphorus mobility—0
Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers—0
Estimation of the mortality rate functions from time series field data in a stage-structured demographic model for Lobesia botrana—0
Blind Deinterleaving of Signals in Time Series with Self-attention Based Soft Min-cost Flow Learning—0
Towards Safe Policy Improvement for Non-Stationary MDPsCode0
Loss-analysis via Attention-scale for Physiologic Time Series—0
Model of continuous random cascade processes in financial markets—0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANs—0
A novel convolutional neural network model to remove muscle artifacts from EEG—0
Prediction of Rainfall in Rajasthan, India using Deep and Wide Neural Network—0
Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy—0
VenoMave: Targeted Poisoning Against Speech RecognitionCode0
A study of the Multicriteria decision analysis based on the time-series features and a TOPSIS method proposal for a tensorial approach—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