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 4201–4225 of 6748 papers

TitleStatusHype
Variational inference of latent state sequences using Recurrent Networks—0
Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps—0
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures—0
Variations on two-parameter families of forecasting functions: seasonal/nonseasonal Models, comparison to the exponential smoothing and ARIMA models, and applications to stock market data—0
Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inference—0
VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder—0
Vehicular Visible Light Communications Noise Analysis and Autoencoder Based Denoising—0
Vertical Power Flow Forecast with LSTMs Using Regular Training Update Strategies—0
Computer Vision Self-supervised Learning Methods on Time Series—0
Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment—0
Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories—0
Video Sentiment Analysis with Bimodal Information-augmented Multi-Head Attention—0
ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformer—0
Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time Series Forecasting—0
VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis—0
Visual Analytics of Movement Pattern Based on Time-Spatial Data: A Neural Net Approach—0
Visual Evaluation of Generative Adversarial Networks for Time Series Data—0
Visual Time Series Forecasting: An Image-driven Approach—0
Visualising Deep Network's Time-Series Representations—0
Visualizing High Dimensional Dynamical Processes—0
Visualizing Parliamentary Speeches as Networks: the DYLEN Tool—0
Visual Time Series Forecasting: An Image-driven Approach—0
ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks—0
Voltage Quality Time Series Classification using Convolutional Neural Network—0
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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