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 4901–4925 of 6748 papers

TitleStatusHype
Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information—0
Modular Federated Learning—0
Solar UV-B/A radiation is highly effective in inactivating SARS-CoV-2—0
MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts—0
Molecular Dynamics of Polymer-lipids in Solution from Supervised Machine Learning—0
Monetary Policy and Wealth Inequalities in Great Britain: Assessing the role of unconventional policies for a decade of household data—0
Monitoring the Dynamic Networks of Stock Returns—0
Monitoring Time Series With Missing Values: a Deep Probabilistic Approach—0
Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series—0
Morphological Change Forecasting for Prostate Glands using Feature-based Registration and Kernel Density Extrapolation—0
Mosquito Detection with Neural Networks: The Buzz of Deep Learning—0
Motif-based Rule Discovery for Predicting Real-valued Time Series—0
Motif Detection Inspired by Immune Memory (JORS)—0
Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification—0
Motif-guided Time Series Counterfactual Explanations—0
Motion ID: Human Authentication Approach—0
Motor imagery classification using EEG spectrograms—0
Movement extraction by detecting dynamics switches and repetitions—0
Moving poselets: A discriminative and interpretable skeletal motion representation for action recognition—0
MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation—0
MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price—0
MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry—0
MTSMAE: Masked Autoencoders for Multivariate Time-Series Forecasting—0
m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series—0
MulBot: Unsupervised Bot Detection Based on Multivariate Time Series—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