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 2801–2825 of 6748 papers

TitleStatusHype
Bitcoin Volatility and Intrinsic Time Using Double Subordinated Levy Processes—0
Influence of Mobility Restrictions on Transmission of COVID-19 in the state of Maryland -- the USA—0
Improving the spectral resolution of fMRI signals through the temporal de-correlation approach—0
Modeling of Low Rank Time Series—0
Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM—0
Distributed Estimation of Sparse Inverse Covariances—0
Deep Learning with Kernel Flow Regularization for Time Series Forecasting—0
High-dimensional regression with potential prior information on variable importanceCode0
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications—0
A Wavelet Method for Panel Models with Jump Discontinuities in the ParametersCode0
Analysis of chaotic dynamical systems with autoencoders—0
Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques—0
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals—0
Learning Predictive and Interpretable Timeseries Summaries from ICU Data—0
Rotor Localization and Phase Mapping of Cardiac Excitation Waves using Deep Neural Networks—0
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series—0
Personalized Online Machine Learning—0
Signal Classification using Smooth Coefficients of Multiple wavelets—0
Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks—0
Early and Revocable Time Series Classification—0
A new look at the anthropogenic global warming consensus: an econometric forecast based on the ARIMA model of paleoclimate series—0
Neural forecasting at scale—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
SFFDD: Deep Neural Network with Enriched Features for Failure Prediction with Its Application to Computer Disk Driver—0
Modeling Regime Shifts in Multiple 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