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 4226–4250 of 6748 papers

TitleStatusHype
Uncertainty Modelling in Risk-averse Supply Chain Systems Using Multi-objective Pareto Optimization—0
Sensor selection on graphs via data-driven node sub-sampling in network time series—0
A Graph-constrained Changepoint Detection Approach for ECG Segmentation—0
Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility—0
Rotated spectral principal component analysis (rsPCA) for identifying dynamical modes of variability in climate systems—0
A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs—0
Memory and forecasting capacities of nonlinear recurrent networks—0
Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting—0
Applications of shapelet transform to time series classification of earthquake, wind and wave data—0
Structural clustering of volatility regimes for dynamic trading strategies—0
A Deep Learning Approach for Motion Forecasting Using 4D OCT Data—0
MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition—0
COVID-19 Time-series Prediction by Joint Dictionary Learning and Online NMFCode0
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac SignalsCode0
A Critical Overview of Privacy-Preserving Approaches for Collaborative Forecasting—0
Network Anomaly Detection based on Tensor Decomposition—0
A Benchmark Study on Time Series Clustering—0
Characters as Graphs: Recognizing Online Handwritten Chinese Characters via Spatial Graph Convolutional Network—0
How macroscopic laws describe complex dynamics: asymptomatic population and CoviD-19 spreading—0
Tree Echo State Autoencoders with GrammarsCode0
Information flow networks of Chinese stock market sectors—0
Kernels for time series with irregularly-spaced multivariate observations—0
Predicting Online Item-choice Behavior: A Shape-restricted Regression Perspective—0
A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models—0
Predictability of Power Grid Frequency—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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