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 1101–1150 of 6748 papers

TitleStatusHype
Automatic Detection Of Noise Events at Shooting Range Using Machine Learning—0
Automatic Detection of Major Freeway Congestion Events Using Wireless Traffic Sensor Data: A Machine Learning Approach—0
A network-based transfer learning approach to improve sales forecasting of new products—0
Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data—0
Automatic deep learning for trend prediction in time series data—0
An Estimation of Online Video User Engagement from Features of Continuous Emotions—0
Quantile Convolutional Neural Networks for Value at Risk Forecasting—0
Automatic Construction of a Recurrent Neural Network based Classifier for Vehicle Passage Detection—0
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition—0
An Equilibrium Model for the Cross-Section of Liquidity Premia—0
Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate—0
Cognitive Computing to Optimize IT Services—0
Inferring Global Dynamics of a Black-Box System Using Machine Learning—0
Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model—0
Automated Testing of AI Models—0
An Ensemble method for Content Selection for Data-to-text Systems—0
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification—0
Cognitive forces shape the dynamics of word usage across multiple languages—0
Cognitive state classification using transformed fMRI data—0
Collaborative Multiobjective Evolutionary Algorithms in search of better Pareto Fronts. An application to trading systems—0
Combining Embeddings and Fuzzy Time Series for High-Dimensional Time Series Forecasting in Internet of Energy Applications—0
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction—0
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks—0
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network—0
An End-to-End Model for Time Series Classification In the Presence of Missing Values—0
Automated Model Selection for Time-Series Anomaly Detection—0
Automated Mobility Context Detection with Inertial Signals—0
Adversarial Attacks on Multivariate Time Series—0
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning—0
Automated Label Generation for Time Series Classification with Representation Learning: Reduction of Label Cost for Training—0
Automated Few-Shot Time Series Forecasting based on Bi-level Programming—0
An Empirical Study of the L2-Boost technique with Echo State Networks—0
A Comparison of Nineteen Various Electricity Consumption Forecasting Approaches and Practicing to Five Different Households in Turkey—0
Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia—0
Automated Diagnosis of Epilepsy Employing Multifractal Detrended Fluctuation Analysis Based Features—0
Automated Detection of Left Ventricle in Arterial Input Function Images for Inline Perfusion Mapping using Deep Learning: A study of 15,000 Patients—0
An empirical study of neural networks for trend detection in time series—0
A Comparison of Model-Free and Model Predictive Control for Price Responsive Water Heaters—0
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
Structural clustering of volatility regimes for dynamic trading strategies—0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection—0
An Empirical Study on How the Developers Discussed about Pandas Topics—0
Autoencoding Time Series for Visualisation—0
Adversarial attacks against Bayesian forecasting dynamic models—0
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting—0
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data—0
CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation—0
Autoencoding Conditional GAN for Portfolio Allocation Diversification—0
Autoencoder-based time series clustering with energy applications—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