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 2701–2725 of 6748 papers

TitleStatusHype
Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future—0
Predictive Modeling of Coronal Hole Areas Using Long Short-Term Memory Networks—0
A Statistical Machine Learning Approach to Yield Curve Forecasting—0
Coresets for Time Series Clustering—0
Coresets for k-Segmentation of Streaming Data—0
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems—0
Coresets for Kernel Regression—0
Core Network Management Procedures for Self-Organized and Sustainable 5G Cellular Networks—0
Non-stationary GARCH modelling for fitting higher order moments of financial series within moving time windows—0
Core-Collapse Supernova Gravitational-Wave Search and Deep Learning Classification—0
A State-Space Approach to Dynamic Nonnegative Matrix Factorization—0
An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions—0
A CNN–LSTM model for gold price time-series forecasting—0
Copy the dynamics using a learning machine—0
A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements—0
Coordinating users of shared facilities via data-driven predictive assistants and game theory—0
Asset volatility forecasting:The optimal decay parameter in the EWMA model—0
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification—0
ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis—0
Asset Pricing with General Transaction Costs: Theory and Numerics—0
Convolution, attention and structure embedding—0
Asset Pricing and Deep Learning—0
An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environments—0
Convolutional-Sparse-Coded Dynamic Mode Decomposition and Its Application to River State Estimation—0
Convolutional Sequence Modeling Revisited—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