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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4001–4050 of 6748 papers

TitleStatusHype
Epileptic Seizure Prediction: A Semi-Dilated Convolutional Neural Network Architecture—0
Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Longitudinal DataCode0
Shape-CD: Change-Point Detection in Time-Series Data with Shapes and Neurons—0
Lasso Inference for High-Dimensional Time Series—0
Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variablesCode0
MAGMA: Inference and Prediction with Multi-Task Gaussian ProcessesCode0
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for BrazilCode0
Permutation-based tests for discontinuities in event studies—0
Time Series Source Separation with Slow Flows—0
Machine Learning a Molecular Hamiltonian for Predicting Electron Dynamics—0
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach—0
Can we Estimate Truck Accident Risk from Telemetric Data using Machine Learning?—0
Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles—0
A fast noise filtering algorithm for time series prediction using recurrent neural networks—0
Leveraging the Self-Transition Probability of Ordinal Pattern Transition Graph for Transportation Mode ClassificationCode0
Attention Mechanism for Multivariate Time Series Recurrent Model Interpretability Applied to the Ironmaking Industry—0
MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry—0
Understanding fluctuations through Multivariate Circulant Singular Spectrum Analysis—0
timeXplain -- A Framework for Explaining the Predictions of Time Series ClassifiersCode0
VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry—0
SRDCNN: Strongly Regularized Deep Convolution Neural Network Architecture for Time-series Sensor Signal Classification Tasks—0
Generating Trading Signals by ML algorithms or time series ones?—0
GeoStat Representations of Time Series for Fast Classification—0
A unified machine learning approach to time series forecasting applied to demand at emergency departments—0
Rewiring the Transformer with Depth-Wise LSTMs—0
Identifying Latent Stochastic Differential EquationsCode0
Prediction of Traffic Flow via Connected Vehicles—0
Multi-future Merchant Transaction Prediction—0
On the variability of functional connectivity and network measures in source-reconstructed EEG time-series—0
Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data—0
Automatic Detection of Major Freeway Congestion Events Using Wireless Traffic Sensor Data: A Machine Learning Approach—0
Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks—0
Graph Signal Processing: Vertex Multiplication—0
Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands—0
Accuracy of neural networks for the simulation of chaotic dynamics: precision of training data vs precision of the algorithm—0
Classification with 2-D Convolutional Neural Networks for breast cancer diagnosisCode0
Multivariate Time Series Classification Using Spiking Neural Networks—0
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural NetworksCode0
Compact representation of temporal processes in echosounder time series via matrix decomposition—0
Dalek -- a deep-learning emulator for TARDIS—0
High-recall causal discovery for autocorrelated time series with latent confounders—0
Inference on the change point in high dimensional time series models via plug in least squares—0
Path Signatures on Lie GroupsCode0
Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential EquationsCode0
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation—0
Semi-supervised Sequential Generative Models—0
Neural Time Warping For Multiple Sequence Alignment—0
Coloured noise time series as appropriate models for environmental variation in artificial evolutionary systems—0
Cyclical Electromechanical Error Denial System Using Matrix Profile—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