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 5201–5250 of 6748 papers

TitleStatusHype
Low-pass filtering as Bayesian inference—0
Bayesian Nonparametric Adaptive Spectral Density Estimation for Financial Time Series—0
Simulating extrapolated dynamics with parameterization networks—0
A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat—0
Decentralized Flood Forecasting Using Deep Neural Networks—0
Investigating Recurrent Neural Network Memory Structures using Neuro-EvolutionCode0
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time WarpingCode0
A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting—0
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden ConfoundersCode0
Optimal Attack against Autoregressive Models by Manipulating the Environment—0
Unsupervised Prediction of Negative Health Events Ahead of Time—0
Unsupervised Scalable Representation Learning for Multivariate Time SeriesCode0
End-to-End Learned Early Classification of Time Series for In-Season Crop Type MappingCode0
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian ComputationCode0
A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms—0
Stochastic Gradient MCMC for Nonlinear State Space ModelsCode0
Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series—0
Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM—0
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks—0
Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm—0
Clustering Discrete-Valued Time Series—0
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics -- A Comprehensive Review—0
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data—0
Portfolio Optimization under Fast Mean-reverting and Rough Fractional Stochastic Environment—0
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series PredictionCode0
Deep-Learning Inversion of Seismic Data—0
Anomaly detection in the dynamics of web and social networksCode0
Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?—0
Online Estimation of Multiple Dynamic Graphs in Pattern Sequences—0
ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-TermCode0
Explainable Failure Predictions with RNN Classifiers based on Time Series Data—0
Effective Combination of DenseNet andBiLSTM for Keyword Spotting—0
Predicting Performance using Approximate State Space Model for Liquid State Machines—0
Kernel Change-point Detection with Auxiliary Deep Generative ModelsCode0
Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference—0
Efficient Matrix Profile Computation Using Different Distance FunctionsCode0
Machine learning with neural networksCode0
Deep learning-based electroencephalography analysis: a systematic reviewCode0
lassopack: Model selection and prediction with regularized regression in Stata—0
Predicting Individual Responses to Vasoactive Medications in Children with Septic Shock—0
Synthesising a Database of Parameterised Linear and Non-Linear Invariants for Time-Series Constraints—0
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial NetworksCode0
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models—0
Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network MethodologyCode0
Forecasting Granular Audience Size for Online Advertising—0
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network—0
Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination—0
Causal Discovery with Attention-Based Convolutional Neural NetworksCode0
A unified framework of epidemic spreading prediction by empirical mode decomposition based ensemble learning techniques—0
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class ClassificationCode0
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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