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 1001–1025 of 6748 papers

TitleStatusHype
Tab2vox: CNN-Based Multivariate Multilevel Demand Forecasting Framework by Tabular-To-Voxel Image Conversion—0
MulBot: Unsupervised Bot Detection Based on Multivariate Time Series—0
PromptCast: A New Prompt-based Learning Paradigm for Time Series ForecastingCode1
Estimation of Shade Losses in Unlabeled PV Data—0
Dataset: Impact Events for Structural Health Monitoring of a Plastic Thin PlateCode1
An Attention Free Long Short-Term Memory for Time Series Forecasting—0
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography—0
The boosted HP filter is more general than you might think—0
Quantifying How Hateful Communities Radicalize Online Users—0
Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques—0
Deep Convolutional Architectures for Extrapolative Forecast in Time-dependent Flow Problems—0
Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series DataCode0
De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs—0
DynaConF: Dynamic Forecasting of Non-Stationary Time SeriesCode0
A review of predictive uncertainty estimation with machine learning—0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data—0
Multi-time Predictions of Wildfire Grid Map using Remote Sensing Local Data—0
Universal abundance fluctuations across microbial communities, tropical forests, and urban populations—0
Understanding of the properties of neural network approaches for transient light curve approximationsCode1
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description—0
Statistical Properties of the Entropy from Ordinal Patterns—0
Efficient learning of nonlinear prediction models with time-series privileged informationCode0
Out-of-Distribution Representation Learning for Time Series Classification—0
Information Theoretic Measures of Causal Influences during Transient Neural Events—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