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

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Showing 3351–3375 of 6748 papers

TitleStatusHype
Spatio-Temporal Graph Scattering Transform—0
Estimating Vector Fields from Noisy Time SeriesCode0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
Learn to Predict Vertical Track Irregularity with Extremely Imbalanced Data—0
LandCoverNet: A global benchmark land cover classification training datasetCode0
Learning summary features of time series for likelihood free inference—0
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential EquationsCode1
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data—0
Forecast with Forecasts: Diversity Matters—0
Inference in mixed causal and noncausal models with generalized Student's t-distributions—0
Recursive Tree Grammar AutoencodersCode0
Concept-based model explanations for Electronic Health RecordsCode0
SAFCAR: Structured Attention Fusion for Compositional Action Recognition—0
Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron ResolutionCode1
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks—0
Fast Automatic Feature Selection for Multi-Period Sliding Window Aggregate in Time Series—0
Named-Entity Based Sentiment Analysis of Nepali News Media Texts—0
Market Comment Generation from Data with Noisy Alignments—0
Assessing Social License to Operate from the Public Discourse on Social Media—0
Modeling Evolution of Message Interaction for Rumor Resolution—0
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart HomesCode0
We are More than Our Joints: Predicting how 3D Bodies Move—0
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting—0
Weak Form Generalized Hamiltonian LearningCode1
Latent Dynamic Factor Analysis of High-Dimensional Neural RecordingsCode0
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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