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 2601–2625 of 6748 papers

TitleStatusHype
Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning—0
Explainable nonlinear modelling of multiple time series with invertible neural networks—0
Online learning of windmill time series using Long Short-term Cognitive Networks—0
National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?—0
Market regime classification with signaturesCode1
MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction—0
Long Short-term Cognitive NetworksCode0
Attaining entropy production and dissipation maps from Brownian movies via neural networksCode0
Continuous Latent Process FlowsCode1
As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learningCode0
FallDeF5: A Fall Detection Framework Using 5G-based Deep Gated Recurrent Unit Networks—0
Evolving-Graph Gaussian ProcessesCode0
Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model—0
False Negative Reduction in Video Instance Segmentation using Uncertainty EstimatesCode0
Capturing the temporal constraints of gradual patternsCode0
On a novel training algorithm for sequence-to-sequence predictive recurrent networks—0
Autonomous Deep Quality Monitoring in Streaming EnvironmentsCode0
Time-Series Representation Learning via Temporal and Contextual ContrastingCode1
Automated Evolutionary Approach for the Design of Composite Machine Learning PipelinesCode1
The mbsts package: Multivariate Bayesian Structural Time Series Models in R—0
Functional Classwise Principal Component Analysis: A Novel Classification Framework—0
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration SensorsCode0
Closed-form Continuous-time Neural ModelsCode2
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model—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