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 3476–3500 of 6748 papers

TitleStatusHype
Creating cloud-free satellite imagery from image time series with deep learning—0
A Lane-Changing Prediction Method Based on Temporal Convolution Network—0
Multiscale characteristics of the emerging global cryptocurrency market—0
Modern strategies for time series regression—0
Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests—0
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection—0
Augmenting transferred representations for stock classification—0
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
Evaluating data augmentation for financial time series classificationCode0
Hybrid Backpropagation Parallel Reservoir Networks—0
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review—0
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting—0
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks—0
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent KernelsCode0
Quasi-steady uptake and bacterial community assembly in a mathematical model of soil-phosphorus mobility—0
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems—0
Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers—0
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
A Systematic Comparison of Forecasting for Gross Domestic Product in an Emergent Economy—0
Estimation of the mortality rate functions from time series field data in a stage-structured demographic model for Lobesia botrana—0
Inter-Series Attention Model for COVID-19 ForecastingCode1
Blind Deinterleaving of Signals in Time Series with Self-attention Based Soft Min-cost Flow Learning—0
Model of continuous random cascade processes in financial markets—0
Loss-analysis via Attention-scale for Physiologic Time Series—0
Towards Safe Policy Improvement for Non-Stationary MDPsCode0
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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