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 2226–2250 of 6748 papers

TitleStatusHype
Prediction Algorithm for Heat Demand of Science and Technology Topics Based on Time Convolution Network—0
Learning latent causal relationships in multiple time series—0
Generative Adversarial Network for Future Hand Segmentation from Egocentric VideoCode0
Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset—0
Diverse Counterfactual Explanations for Anomaly Detection in Time Series—0
Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series—0
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies—0
Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in Delay-embedded Space—0
Selection of entropy based features for the analysis of the Archimedes' spiral applied to essential tremor—0
Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study—0
Transfer learning for cross-modal demand prediction of bike-share and public transit—0
Ranking of Communities in Multiplex Spatiotemporal Models of Brain Dynamics—0
Joint Time-Vertex Fractional Fourier Transform—0
What is the best RNN-cell structure for forecasting each time series behavior?—0
Time-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis—0
Reducing overestimating and underestimating volatility via the augmented blending-ARCH model—0
Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition—0
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions—0
Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time SeriesCode0
Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment—0
A Joint-Entropy Approach To Time-series ClassificationCode0
Dual reparametrized Variational Generative Model for Time-Series Forecasting—0
Parameter Inference of Time Series by Delay Embeddings and Learning Differentiable Operators—0
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the PastCode0
TiSAT: Time Series Anomaly TransformerCode0
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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