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 526–550 of 6748 papers

TitleStatusHype
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting EpidemicsCode1
A Synthetic Texas Power System with Time-Series Weather-Dependent Spatiotemporal ProfilesCode1
Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep LearningCode1
Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural NetworkCode1
Efficient recurrent architectures through activity sparsity and sparse back-propagation through timeCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
Efficient implementations of echo state network cross-validationCode1
Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement PredictionCode1
Efficient Cross-Validation of Echo State NetworksCode1
General Evaluation for Instruction Conditioned Navigation using Dynamic Time WarpingCode1
Efficient data-driven gap filling of satellite image time series using deep neural networks with partial convolutionsCode1
Efficient Optimization of Echo State Networks for Time Series DatasetsCode1
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning InfusionCode1
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load ForecastingCode1
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent AutoencodersCode1
Dynamic Sparse Network for Time Series Classification: Learning What to "see''Code1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
A Time-dependent SIR model for COVID-19 with Undetectable Infected PersonsCode1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality ModelingCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series ClassificationCode1
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty QuantificationCode1
Dynamic Graph Learning-Neural Network for Multivariate Time Series ModelingCode1
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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