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 1951–1975 of 6748 papers

TitleStatusHype
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling—0
Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model—0
Comparison of PCA with ICA from data distribution perspective—0
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving—0
Detecting Gas Vapor Leaks Using Uncalibrated Sensors—0
Detecting Handwritten Mathematical Terms with Sensor Based Data—0
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning—0
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization—0
Detecting residues of cosmic events using residual neural network—0
Detecting Rough Volatility: A Filtering Approach—0
Detecting Slag Formations with Deep Convolutional Neural Networks—0
Detecting Structural Breaks in Foreign Exchange Markets by using the group LASSO technique—0
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings—0
A Review of Mathematical and Computational Methods in Cancer Dynamics—0
Comparison of Machine Learning Methods for Predicting Karst Spring Discharge in North China—0
The amplitude modulation pattern of Gaussian noise is a fingerprint of Gaussianity—0
Comparison of LSTM autoencoder based deep learning enabled Bayesian inference using two time series reconstruction approaches—0
American Hate Crime Trends Prediction with Event Extraction—0
Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network—0
Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection—0
Bayesian nonparametric sparse VAR models—0
Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patients—0
Do Word Embeddings Really Understand Loughran-McDonald's Polarities?—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