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 4076–4100 of 6748 papers

TitleStatusHype
STEER: Simple Temporal Regularization For Neural ODEs—0
The Dilemma Between Data Transformations and Adversarial Robustness for Time Series Application Systems—0
PECAIQR: A Model for Infectious Disease Applied to the Covid-19 Epidemic—0
Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments—0
Learning-Based Real-Time Event Identification Using Rich Real PMU Data—0
Longitudinal Variational Autoencoder—0
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes—0
Analysing the resilience of the European commodity production system with PyResPro, the Python Production Resilience package—0
Prior knowledge distillation based on financial time series—0
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction—0
A Multi-Phase Approach for Product Hierarchy Forecasting in Supply Chain Management: Application to MonarchFx Inc—0
A Deterministic Approximation to Neural SDEs—0
Optimisation of non-pharmaceutical measures in COVID-19 growth via neural networks—0
A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data—0
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm—0
Dynamic Window-level Granger Causality of Multi-channel Time Series—0
Tempered Stable Processes with Time Varying Exponential Tails—0
Interpretable Super-Resolution via a Learned Time-Series Representation—0
FedGAN: Federated Generative Adversarial Networks for Distributed Data—0
Fairness in Forecasting and Learning Linear Dynamical Systems—0
Scoring and Assessment in Medical VR Training Simulators with Dynamic Time Series Classification—0
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
Learning Continuous-Time Dynamics by Stochastic Differential Networks—0
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting—0
Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory—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