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 3451–3500 of 6748 papers

TitleStatusHype
Predicting the 2020 US Presidential Election with Twitter—0
Predicting the Biological Classification of Cell-Cycle Regulated Genes of Saccharomyces cerevisiae using Community Detection Algorithms on Gene Co-expression Networks—0
Predicting the Future is like Completing a Painting!—0
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models—0
Predicting the outcome of team movements -- Player time series analysis using fuzzy and deep methods for representation learning—0
Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots—0
Predicting the temporal dynamics of turbulent channels through deep learning—0
Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model—0
Predicting Unplanned Readmissions in the Intensive Care Unit: A Multimodality Evaluation—0
Prediction Algorithm for Heat Demand of Science and Technology Topics Based on Time Convolution Network—0
Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People—0
On Learning Prediction-Focused Mixtures—0
Prediction of adverse events in Afghanistan: regression analysis of time series data grouped not by geographic dependencies—0
Prediction of Dynamical time Series Using Kernel Based Regression and Smooth Splines—0
Prediction of financial time series using LSTM and data denoising methods—0
Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS—0
Prediction of gene expression time series and structural analysis of gene regulatory networks using recurrent neural networks—0
Prediction of good reaction coordinates and future evolution of MD trajectories using Regularized Sparse Autoencoders: A novel deep learning approach—0
Prediction of hierarchical time series using structured regularization and its application to artificial neural networks—0
Prediction of Hilbertian autoregressive processes : a Recurrent Neural Network approach—0
Prediction of MRI Hardware Failures based on Image Features using Time Series Classification—0
Prediction of Rainfall in Rajasthan, India using Deep and Wide Neural Network—0
Prediction of Sea Surface Temperature using Long Short-Term Memory—0
Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for continuous earth surface monitoring—0
Prediction of short and long-term droughts using artificial neural networks and hydro-meteorological variables—0
Prediction of Traffic Flow via Connected Vehicles—0
Prediction with Spatio-temporal Point Processes with Self Organizing Decision Trees—0
Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University—0
Predictive-Corrective Networks for Action Detection—0
Predictive Maintenance for General Aviation Using Convolutional Transformers—0
Predictive Process Model Monitoring using Recurrent Neural Networks—0
Equivariant online predictions of non-stationary time series—0
Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series—0
Preparing fMRI Data for Statistical Analysis—0
Pre-treatment of outliers and anomalies in plant data: Methodology and case study of a Vacuum Distillation Unit—0
Preventing Gradient Explosions in Gated Recurrent Units—0
Previsão dos preços de abertura, mínima e máxima de índices de mercados financeiros usando a associação de redes neurais LSTM—0
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows—0
Prior knowledge distillation based on financial time series—0
Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data—0
Automatic Financial Feature Construction—0
Privacy Amplification by Subsampling in Time Domain—0
Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning—0
Privacy-Friendly Mobility Analytics using Aggregate Location Data—0
Privacy Meets Explainability: A Comprehensive Impact Benchmark—0
Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach—0
Probabilistic Broken-Stick Model: A Regression Algorithm for Irregularly Sampled Data with Application to eGFR—0
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach—0
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography—0
Probabilistic Forecasting: A Level-Set Approach—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