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 626–650 of 6748 papers

TitleStatusHype
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdownCode1
Crop mapping from image time series: deep learning with multi-scale label hierarchiesCode1
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural NetworksCode1
Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and OutliersCode1
Network of Tensor Time SeriesCode1
Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time SeriesCode1
Domain Adaptation for Time Series Forecasting via Attention SharingCode1
Early Abandoning and Pruning for Elastic Distances including Dynamic Time WarpingCode1
Last Query Transformer RNN for knowledge tracingCode1
NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series ForecastingCode1
MALI: A memory efficient and reverse accurate integrator for Neural ODEsCode1
FedMood: Federated Learning on Mobile Health Data for Mood DetectionCode1
Deep Semi-Supervised Learning for Time Series ClassificationCode1
NRTSI: Non-Recurrent Time Series ImputationCode1
CKConv: Continuous Kernel Convolution For Sequential DataCode1
Uncertain Time Series Classification With Shapelet TransformCode1
MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationCode1
Adjusting for Autocorrelated Errors in Neural Networks for Time SeriesCode1
A Review of Graph Neural Networks and Their Applications in Power SystemsCode1
Multi-Time Attention Networks for Irregularly Sampled Time SeriesCode1
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
Bayesian hierarchical stacking: Some models are (somewhere) usefulCode1
Interpretable Models for Granger Causality Using Self-explaining Neural NetworksCode1
Discrete Graph Structure Learning for Forecasting Multiple Time SeriesCode1
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro DataCode1
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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