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 3551–3600 of 6748 papers

TitleStatusHype
Quantification of metabolic niche occupancy dynamics in a Baltic Sea bacterial community—0
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference—0
Quantifying How Hateful Communities Radicalize Online Users—0
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs—0
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain—0
Quantifying Synchronization in a Biologically Inspired Neural Network—0
Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach—0
Quantifying the Effects of COVID-19 on Restaurant Reviews—0
Quantifying Volatility Reduction in German Day-ahead Spot Market in the Period 2006 through 2016—0
Quantile Autoregression-based Non-causality Testing—0
Quantile-based fuzzy clustering of multivariate time series in the frequency domain—0
Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques—0
Quantile Correlations: Uncovering temporal dependencies in financial time series—0
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data—0
Quantitative Evaluation of Full-Scale Ship Maneuvering Characteristics During Berthing and Unberthing—0
Quantitative evaluation of the performance of discrete-time reservoir computers in the forecasting, filtering, and reconstruction of stochastic stationary signals—0
Quantum Bohmian Inspired Potential to Model Non-Gaussian Events and the Application in Financial Markets—0
Quantum Gates and Quantum Circuits of Stock Portfolio—0
Quantum Persistent Homology for Time Series—0
Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Series—0
Quasi-steady uptake and bacterial community assembly in a mathematical model of soil-phosphorus mobility—0
A Query-Response Causal Analysis of Reaction Events in Biochemical Reaction Networks—0
Quick and Easy Time Series Generation with Established Image-based GANs—0
Quickest Detection of COVID-19 Pandemic Onset—0
Quick Line Outage Identification in Urban Distribution Grids via Smart Meters—0
Quick, Stat!: A Statistical Analysis of the Quick, Draw! Dataset—0
R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting—0
Rademacher complexity of stationary sequences—0
Random cohort effects and age groups dependency structure for mortality modelling and forecasting: Mixed-effects time-series model approach—0
Random Feature Approximation for Online Nonlinear Graph Topology Identification—0
Random Fragments Classification of Microbial Marker Clades with Multi-class SVM and N-Best Algorithm—0
Randomized kernels for large scale Earth observation applications—0
Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality—0
On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics—0
Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series—0
Random matrix approach to estimation of high-dimensional factor models—0
Random pattern and frequency generation using a photonic reservoir computer with output feedback—0
Random Projection Filter Bank for Time Series Data—0
Random selection of factors preserves the correlation structure in a linear factor model to a high degree—0
Random Similarity Forests—0
Random thoughts about Complexity, Data and Models—0
Random vector functional link neural network based ensemble deep learning for short-term load forecasting—0
Ranking and significance of variable-length similarity-based time series motifs—0
Ranking election issues through the lens of social media—0
Ranking of Communities in Multiplex Spatiotemporal Models of Brain Dynamics—0
RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning—0
RapidAI4EO: Mono- and Multi-temporal Deep Learning models for Updating the CORINE Land Cover Product—0
RAPID: Early Classification of Explosive Transients using Deep Learning—0
Rapidly evaluating lockdown strategies using spectral analysis: the cycles behind new daily COVID-19 cases and what happens after lockdown—0
Rapid Time Series Prediction with a Hardware-Based Reservoir Computer—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