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 176–200 of 6748 papers

TitleStatusHype
Classification of Long Sequential Data using Circular Dilated Convolutional Neural NetworksCode1
Conditional Sig-Wasserstein GANs for Time Series GenerationCode1
Continuous-Time Deep Glioma Growth ModelsCode1
COVID-19 Data Analysis and Forecasting: Algeria and the WorldCode1
Deep ConvLSTM with self-attention for human activity decoding using wearablesCode1
A Deep Learning Approach to Analyzing Continuous-Time SystemsCode1
Changing Fashion CulturesCode1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking ApplicationsCode1
catch22: CAnonical Time-series CHaracteristicsCode1
Data-driven discovery of intrinsic dynamicsCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time seriesCode1
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
Closed-Form Diffeomorphic Transformations for Time Series AlignmentCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Color-aware two-branch DCNN for efficient plant disease classificationCode1
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural NetworksCode1
Calibration of Google Trends Time SeriesCode1
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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