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

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Showing 5251–5300 of 6748 papers

TitleStatusHype
Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data—0
Optimal Stopping with Gaussian Processes—0
Optimal Time-Series Motifs—0
Optimal trading strategies - a time series approach—0
Optimal Transport Based Change Point Detection and Time Series Segment Clustering—0
Optimal Transport vs. Fisher-Rao distance between Copulas for Clustering Multivariate Time Series—0
Optimal Warping Paths are unique for almost every Pair of Time Series—0
Optimisation of non-pharmaceutical measures in COVID-19 growth via neural networks—0
Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR—0
Optimizing a Bayesian method for estimating the Hurst exponent in behavioral sciences—0
Optimizing and Contrasting Recurrent Neural Network Architectures—0
Optimizing Convergence for Iterative Learning of ARIMA for Stationary Time Series—0
Optimizing Expected Shortfall under an _1 constraint -- an analytic approach—0
Optimizing Hyperparameters in CNNs using Bilevel Programming in Time Series Data—0
Optimizing Returns Using the Hurst Exponent and Q Learning on Momentum and Mean Reversion Strategies—0
Optimizing Temporal Convolutional Network inference on FPGA-based accelerators—0
Optimizing the Union of Intersections LASSO (UoI_LASSO) and Vector Autoregressive (UoI_VAR) Algorithms for Improved Statistical Estimation at Scale—0
Option Pricing under Fast-varying and Rough Stochastic Volatility—0
Order flow in the financial markets from the perspective of the Fractional Lévy stable motion—0
Order patterns, their variation and change points in financial time series and Brownian motion—0
Ordinal methods for a characterization of evolving functional brain networks—0
Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series—0
Ordinal Synchronization: Using ordinal patterns to capture interdependencies between time series—0
Ordinal time series analysis with the R package otsfeatures—0
Orthogonal Echo State Networks and stochastic evaluations of likelihoods—0
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence—0
OSTSC: Over Sampling for Time Series Classification in R—0
Otimizacao de pesos e funcoes de ativacao de redes neurais aplicadas na previsao de series temporais—0
Outliagnostics: Visualizing Temporal Discrepancy in Outlying Signatures of Data Entries—0
Outlier Detection as Instance Selection Method for Feature Selection in Time Series Classification—0
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series—0
Overcoming limited battery data challenges: A coupled neural network approach—0
Overlooked Poses Actually Make Sense: Distilling Privileged Knowledge for Human Motion Prediction—0
P2ExNet: Patch-based Prototype Explanation Network—0
Page time and the order parameter for a consciousness state—0
PAMOCAT: Automatic retrieval of specified postures—0
Parallel bandit architecture based on laser chaos for reinforcement learning—0
Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting—0
Parallel Machine Learning for Forecasting the Dynamics of Complex Networks—0
Photonic reservoir computer based on frequency multiplexing—0
Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Prediction—0
Parameter-Covariance Maximum Likelihood Estimation—0
Parameter inference in a computational model of hemodynamics in pulmonary hypertension—0
Parameter Inference of Time Series by Delay Embeddings and Learning Differentiable Operators—0
Parameterization of state duration in Hidden semi-Markov Models: an application in electrocardiography—0
Parameterizing the cost function of Dynamic Time Warping with application to time series classification—0
Parametric measures of variability induced by risk measures—0
PARIS: Personalized Activity Recommendation for Improving Sleep Quality—0
Parkinson's Disease Diagnosis Using Deep Learning—0
Parsimonious modeling with Information Filtering Networks—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