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 4901–4950 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
Multi-adaptive Natural Language Generation using Principal Component Regression—0
Multiclass Classification of Cervical Cancer Tissues by Hidden Markov Model—0
Multi-Sample Online Learning for Probabilistic Spiking Neural Networks—0
Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks—0
Multi-Decoder RNN Autoencoder Based on Variational Bayes Method—0
Multidimensional dynamic factor models—0
Multi-dimensional Graph Fourier Transform—0
Multi-Faceted Representation Learning with Hybrid Architecture for Time Series Classification—0
Multifractal analysis of the time series of daily means of wind speed in complex regions—0
Multifractal characterization of gold market: a multifractal detrended fluctuation analysis—0
Multifractal cross-correlations between the World Oil and other Financial Markets in 2012-2017—0
Multifractal cross-correlations of bitcoin and ether trading characteristics in the post-COVID-19 time—0
Multifractal Flexibly Detrended Fluctuation Analysis—0
Multi-future Merchant Transaction Prediction—0
Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction—0
Multi-horizon solar radiation forecasting for Mediterranean locations using time series models—0
Multi-label Prediction in Time Series Data using Deep Neural Networks—0
Multi-Level Association Rule Mining for Wireless Network Time Series Data—0
Multilinear Dynamical Systems for Tensor Time Series—0
Multilingual Dynamic Topic Model—0
Multi-modal Affect Analysis using standardized data within subjects in the Wild—0
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies—0
Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution—0
Multimodal Meta-Learning for Time Series Regression—0
Multimodal Neural Network For Demand 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