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 2176–2200 of 6748 papers

TitleStatusHype
Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection—0
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions—0
A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction—0
Deep Video Prediction for Time Series Forecasting—0
A Wavelet, AR and SVM based hybrid method for short-term wind speed prediction—0
An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System—0
Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives—0
A wavelet analysis of inter-dependence, contagion and long memory among global equity markets—0
DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction—0
Deep Transformer Networks for Time Series Classification: The NPP Safety Case—0
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series—0
An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification—0
A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour—0
Deep Transformer Model with Pre-Layer Normalization for COVID-19 Growth Prediction—0
A walk through of time series analysis on quantum computers—0
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals—0
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO—0
A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse—0
Deep Time Series Models for Scarce Data—0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
A Visibility Graph Averaging Aggregation Operator—0
A New Unified Deep Learning Approach with Decomposition-Reconstruction-Ensemble Framework for Time Series Forecasting—0
Adversarial Unsupervised Representation Learning for Activity Time-Series—0
A conditional likelihood is required to estimate the selection coefficient in ancient DNA—0
A Benchmark Study on Time Series Clustering—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