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 2426–2450 of 6748 papers

TitleStatusHype
An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life predictionCode1
Attentive Neural Controlled Differential Equations for Time-series Classification and ForecastingCode1
A Multi-view Multi-task Learning Framework for Multi-variate Time Series ForecastingCode1
Accurate shape and phase averaging of time series through Dynamic Time Warping—0
MrSQM: Fast Time Series Classification with Symbolic RepresentationsCode1
Computer Vision Self-supervised Learning Methods on Time Series—0
Transformer Networks for Data Augmentation of Human Physical Activity RecognitionCode1
A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection—0
Bilinear Input Normalization for Neural Networks in Financial ForecastingCode1
Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples—0
Time Series Prediction using Deep Learning Methods in Healthcare—0
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures—0
TCCT: Tightly-Coupled Convolutional Transformer on Time Series ForecastingCode1
Parallel Machine Learning for Forecasting the Dynamics of Complex Networks—0
Anomaly Detection on IT Operation Series via Online Matrix Profile—0
Magnetic Field Sensing for Pedestrian and Robot Indoor Positioning—0
SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations—0
Sketches for Time-Dependent Machine LearningCode0
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems—0
Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach—0
S&P 500 Stock Price Prediction Using Technical, Fundamental and Text DataCode1
Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models—0
DTWSSE: Data Augmentation with a Siamese Encoder for Time Series—0
Evolutionary Ensemble Learning for Multivariate Time Series Prediction—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