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 1676–1700 of 6748 papers

TitleStatusHype
DDPG based on multi-scale strokes for financial time series trading strategy—0
A minor extension of the logistic equation for growth of word counts on online media: Parametric description of diversity of growth phenomena in society—0
Decentralized Flood Forecasting Using Deep Neural Networks—0
Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks—0
Decision-Aware Conditional GANs for Time Series Data—0
Decoding Causality by Fictitious VAR Modeling—0
Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data—0
Decoding Financial Health in Kenyas' Medical Insurance Sector: A Data-Driven Cluster Analysis—0
An Attention Free Long Short-Term Memory for Time Series Forecasting—0
Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Models—0
Decoding of visual-related information from the human EEG using an end-to-end deep learning approach—0
Computer activity learning from system call time series—0
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases—0
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Comprehensive Time-Series Regression Models Using GRETL -- U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013—0
Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks—0
Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector—0
A metric to compare the anatomy variation between image time series—0
Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition—0
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems—0
Comprehensive Review of Neural Differential Equations for Time Series Analysis—0
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions—0
Deep Air Quality Forecasting Using Hybrid Deep Learning Framework—0
Accurate shape and phase averaging of time series through Dynamic Time Warping—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