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 3601–3650 of 6748 papers

TitleStatusHype
A deep network approach to multitemporal cloud detection—0
Automatic Registration and Clustering of Time Series—0
Topology Identification under Spatially Correlated Noise—0
An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System—0
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks—0
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution RobustnessCode0
An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth—0
Dynamic Clustering in Federated Learning—0
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous SensorsCode0
Randomized kernels for large scale Earth observation applications—0
A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network—0
Physics-Aware Gaussian Processes in Remote Sensing—0
Spatio-Temporal Graph Scattering Transform—0
Modified Auto Regressive Technique for Univariate Time Series Prediction of Solar Irradiance—0
Estimating Vector Fields from Noisy Time SeriesCode0
[Re] Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-AttentionCode0
Learn to Predict Vertical Track Irregularity with Extremely Imbalanced Data—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
LandCoverNet: A global benchmark land cover classification training datasetCode0
Learning summary features of time series for likelihood free inference—0
Inference in mixed causal and noncausal models with generalized Student's t-distributions—0
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data—0
Concept-based model explanations for Electronic Health RecordsCode0
Forecast with Forecasts: Diversity Matters—0
SAFCAR: Structured Attention Fusion for Compositional Action Recognition—0
Recursive Tree Grammar AutoencodersCode0
Fast Automatic Feature Selection for Multi-Period Sliding Window Aggregate in Time Series—0
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks—0
Assessing Social License to Operate from the Public Discourse on Social Media—0
Training Linear Finite-State Machines—0
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting—0
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart HomesCode0
What went wrong and when?\\ Instance-wise feature importance for time-series black-box models—0
We are More than Our Joints: Predicting how 3D Bodies Move—0
Normalizing Kalman Filters for Multivariate Time Series Analysis—0
Named-Entity Based Sentiment Analysis of Nepali News Media Texts—0
Market Comment Generation from Data with Noisy Alignments—0
STEER : Simple Temporal Regularization For Neural ODE—0
Modeling Evolution of Message Interaction for Rumor Resolution—0
Latent Dynamic Factor Analysis of High-Dimensional Neural RecordingsCode0
Causal analysis of Covid-19 Spread in Germany—0
Unfolding recurrence by Green’s functions for optimized reservoir computing—0
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series—0
Exoplanet Detection using Machine LearningCode0
An Equilibrium Model for the Cross-Section of Liquidity Premia—0
Nowcasting NetworksCode0
Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series PredictionCode0
Unsupervised learning for economic risk evaluation in the context of Covid-19 pandemic—0
Functional Time Series Forecasting: Functional Singular Spectrum Analysis Approaches—0
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach—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