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 4926–4950 of 6748 papers

TitleStatusHype
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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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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