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 2476–2500 of 6748 papers

TitleStatusHype
Active Learning of Driving Scenario Trajectories—0
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration DataCode0
Local Exceptionality Detection in Time Series Using Subgroup Discovery—0
A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse—0
Multimodal Meta-Learning for Time Series Regression—0
Pattern Recognition in Vital Signs Using Spectrograms—0
A New State-of-the-Art Transformers-Based Load Forecaster on the Smart Grid Domain—0
High dimensional Bayesian Optimization Algorithm for Complex System in Time Series—0
Bayesian forecast combination using time-varying features—0
Random Convolution Kernels with Multi-Scale Decomposition for Preterm EEG Inter-burst DetectionCode0
Reconstructing a dynamical system and forecasting time series by self-consistent deep learning—0
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data—0
Dynamic Prediction Model for NOx Emission of SCR System Based on Hybrid Data-driven Algorithms—0
An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions—0
Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators—0
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series—0
Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network—0
Using Deep Learning to Correlate Reddit Posts with Economic Time Series During the COVID-19 Pandemic—0
Predicting in-hospital mortality by combining clinical notes with time-series dataCode1
Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia ClassificationCode1
Understanding the merging behavior patterns and evolutionary mechanism at freeway on-ramps—0
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural NetworksCode1
Opinion Prediction with User FingerprintingCode0
Statistical learning method for predicting density-matrix based electron dynamics—0
Random vector functional link neural network based ensemble deep learning for short-term load forecasting—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