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 5151–5200 of 6748 papers

TitleStatusHype
Generalisation in fully-connected neural networks for time series forecasting—0
WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing DetectionCode0
Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor—0
Weighted Tensor Completion for Time-Series Causal InferenceCode0
Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing—0
RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter—0
Adversarial Generation of Time-Frequency Features with application in audio synthesisCode0
Simulating extrapolated dynamics with parameterization networks—0
Low-pass filtering as Bayesian inference—0
Bayesian Nonparametric Adaptive Spectral Density Estimation for Financial Time Series—0
A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat—0
Investigating Recurrent Neural Network Memory Structures using Neuro-EvolutionCode0
Decentralized Flood Forecasting Using Deep Neural Networks—0
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
Optimal Attack against Autoregressive Models by Manipulating the Environment—0
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden ConfoundersCode0
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
A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms—0
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks—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
catch22: CAnonical Time-series CHaracteristicsCode1
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian ComputationCode0
Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm—0
Clustering Discrete-Valued Time Series—0
Portfolio Optimization under Fast Mean-reverting and Rough Fractional Stochastic Environment—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
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
Spatiotemporal Multi-Graph Convolution Networkfor Ride-hailing Demand ForecastingCode1
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
Machine learning with neural networksCode0
Efficient Matrix Profile Computation Using Different Distance FunctionsCode0
Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference—0
lassopack: Model selection and prediction with regularized regression in Stata—0
Deep learning-based electroencephalography analysis: a systematic reviewCode0
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
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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