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 951–975 of 6748 papers

TitleStatusHype
A Fast Evidential Approach for Stock Forecasting—0
A Consistent Method for Learning OOMs from Asymptotically Stationary Time Series Data Containing Missing Values—0
A fast algorithm for complex discord searches in time series: HOT SAX Time—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model—0
Backpropagation on Dynamical Networks—0
Backpropagation-Free Learning Method for Correlated Fuzzy Neural Networks—0
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models—0
The leverage effect and other stylized facts displayed by Bitcoin returns—0
Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic—0
Causal analysis of Covid-19 Spread in Germany—0
Causal Compression—0
ANFIS-based prediction of power generation for combined cycle power plant—0
Independence Testing for Temporal Data—0
Bag of Recurrence Patterns Representation for Time-Series Classification—0
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition—0
Bandwidth-efficient distributed neural network architectures with application to body sensor networks—0
Basic Filters for Convolutional Neural Networks Applied to Music: Training or Design?—0
Impulse data models for the inverse problem of electrocardiography—0
Bayesian Alignments of Warped Multi-Output Gaussian Processes—0
Bayesian autoregressive spectral estimation—0
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets—0
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior—0
Bayesian Filtering for Multi-period Mean-Variance Portfolio Selection—0
Causal Discovery from Subsampled Time Series Data by Constraint Optimization—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