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 3151–3200 of 6748 papers

TitleStatusHype
On the Time Series Length for an Accurate Fractal Analysis in Network Systems—0
Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange ResidualCode0
Granger Causality: A Review and Recent Advances—0
Order flow in the financial markets from the perspective of the Fractional Lévy stable motion—0
Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases—0
Reconstructing shared dynamics with a deep neural networkCode0
Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and AutoencodersCode0
An Estimation of Online Video User Engagement from Features of Continuous Emotions—0
Stock Price Forecasting in Presence of Covid-19 Pandemic and Evaluating Performances of Machine Learning Models for Time-Series Forecasting—0
A Two-Stage Coordinative Zonal Volt/VAR Control Scheme for Distribution Systems with High Inverter-based Resources—0
Signal automata and hidden Markov models—0
TimeGym: Debugging for Time Series Modeling in Python—0
Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks—0
COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction—0
Context-aware demand prediction in bike sharing systems: incorporating spatial, meteorological and calendrical context—0
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series—0
MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price—0
Comparison Analysis of Facebook's Prophet, Amazon's DeepAR+ and CNN-QR Algorithms for Successful Real-World Sales Forecasting—0
All-Clear Flare Prediction Using Interval-based Time Series Classifiers—0
Explaining Outcomes of Multi-Party Dialogues using Causal Learning—0
Process Model Forecasting Using Time Series Analysis of Event Sequence DataCode0
TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data—0
RotLSTM: Rotating Memories in Recurrent Neural Networks—0
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noiseCode0
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation—0
Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks—0
PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback—0
Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS—0
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy—0
Defined the predictors of the lightning over India by using artificial neural network—0
Nonparametric Test for Volatility in Clustered Multiple Time Series—0
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods—0
Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (2)—0
Initializing LSTM internal states via manifold learning—0
Early Classification of Time Series is Meaningful—0
Phenotyping OSA: a time series analysis using fuzzy clustering and persistent homology—0
Generative modeling of spatio-temporal weather patterns with extreme event conditioning—0
Stochastic Recurrent Neural Network for Multistep Time Series Forecasting—0
Bridging observation, theory and numerical simulation of the ocean using Machine Learning—0
tsrobprep - an R package for robust preprocessing of time series data—0
System identification using Bayesian neural networks with nonparametric noise models—0
Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Data—0
Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed—0
Deep Convolutional Neural Network for Non-rigid Image Registration—0
Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect—0
Learning Hamiltonian dynamics by reservoir computer—0
Neural circuits for dynamics-based segmentation of time seriesCode0
Predicting the Number of Reported Bugs in a Software RepositoryCode0
Normalized multivariate time series causality analysis and causal graph reconstruction—0
Sequential convolutional network for behavioral pattern extraction in gait recognition—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