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 6301–6325 of 6748 papers

TitleStatusHype
Segmenting Hybrid Trajectories using Latent ODEsCode0
XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture ClassificationCode0
Wasserstein Index Generation Model: Automatic Generation of Time-series Index with Application to Economic Policy UncertaintyCode0
Few-shot human motion prediction for heterogeneous sensorsCode0
Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance MatricesCode0
Towards Safe Policy Improvement for Non-Stationary MDPsCode0
fETSmcs: Feature-based ETS model component selectionCode0
Machine Learning vs Statistical Methods for Time Series Forecasting: Size MattersCode0
PI-Net: A Deep Learning Approach to Extract Topological Persistence ImagesCode0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
Analysis of Thai Capital Market Linkages: Part I. Bivariate Copula ApproachCode0
Deep Normed Embeddings for Patient RepresentationCode0
A projected nonlinear state-space model for forecasting time series signalsCode0
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial NetworksCode0
Feature space approximation for kernel-based supervised learningCode0
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 MethodsCode0
MAGMA: Inference and Prediction with Multi-Task Gaussian ProcessesCode0
Wasserstein multivariate auto-regressive models for modeling distributional time seriesCode0
PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time SeriesCode0
Self-attention for raw optical Satellite Time Series ClassificationCode0
Feature Selection for Multivariate Time Series via Network PruningCode0
Feature engineering workflow for activity recognition from synchronized inertial measurement unitsCode0
Autoregressive Convolutional Neural Networks for Asynchronous Time SeriesCode0
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep NetworksCode0
Policy Analysis using Synthetic Controls in Continuous-TimeCode0
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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