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 4051–4100 of 6748 papers

TitleStatusHype
Risk Management and Return Prediction—0
Development of an Algorithm for Identifying Changes in System Dynamics from Time Series—0
Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN—0
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images—0
Forecasting Precipitable Water Vapor Using LSTMsCode0
A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic Programming—0
Improving MF-DFA model with applications in precious metals market—0
Covariance-engaged Classification of Sets via Linear Programming—0
Combining Ensemble Kalman Filter and Reservoir Computing to predict spatio-temporal chaotic systems from imperfect observations and models—0
A Model of the Fed's View on InflationCode0
Line Spectrum Representation for Vector Processes With Application to Frequency Estimation—0
Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning—0
On Multivariate Singular Spectrum Analysis and its Variants—0
Physics-informed machine learning for sensor fault detection with flight test data—0
A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix FactorizationCode0
Short-Term Traffic Forecasting Using High-Resolution Traffic Data—0
A Data-driven Market Simulator for Small Data Environments—0
Chaos may enhance expressivity in cerebellar granular layer—0
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence FunctionsCode0
High-Frequency Radar Ocean Current Mapping at Rapid Scale with Autoregressive Modeling—0
Semi-supervised time series classification method for quantum computing—0
Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series ModelingCode0
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process—0
Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion—0
Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis—0
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