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 3801–3850 of 6748 papers

TitleStatusHype
Multinomial Sampling for Hierarchical Change-Point Detection—0
bootUR: An R Package for Bootstrap Unit Root Tests—0
Hide-and-Seek Privacy ChallengeCode0
Deep Dynamic Factor ModelsCode1
Multi-Sample Online Learning for Probabilistic Spiking Neural Networks—0
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News—0
Epileptic Seizure Prediction: A Semi-Dilated Convolutional Neural Network Architecture—0
Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children—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
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for BrazilCode0
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
Permutation-based tests for discontinuities in event studies—0
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series DataCode1
Time Series Source Separation with Slow Flows—0
COVID-19 Data Analysis and Forecasting: Algeria and the WorldCode1
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach—0
Machine Learning a Molecular Hamiltonian for Predicting Electron Dynamics—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
Leveraging the Self-Transition Probability of Ordinal Pattern Transition Graph for Transportation Mode ClassificationCode0
A fast noise filtering algorithm for time series prediction using recurrent neural networks—0
Understanding fluctuations through Multivariate Circulant Singular Spectrum Analysis—0
Attention Mechanism for Multivariate Time Series Recurrent Model Interpretability Applied to the Ironmaking Industry—0
VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry—0
timeXplain -- A Framework for Explaining the Predictions of Time Series ClassifiersCode0
MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry—0
Generating Trading Signals by ML algorithms or time series ones?—0
Modeling Financial Time Series using LSTM with Trainable Initial Hidden StatesCode1
SRDCNN: Strongly Regularized Deep Convolution Neural Network Architecture for Time-series Sensor Signal Classification Tasks—0
A unified machine learning approach to time series forecasting applied to demand at emergency departments—0
GeoStat Representations of Time Series for Fast Classification—0
Rewiring the Transformer with Depth-Wise LSTMs—0
Identifying Latent Stochastic Differential EquationsCode0
On the variability of functional connectivity and network measures in source-reconstructed EEG time-series—0
Multi-future Merchant Transaction Prediction—0
Prediction of Traffic Flow via Connected Vehicles—0
Graph Signal Processing: Vertex Multiplication—0
Fast Variational Learning in State-Space Gaussian Process ModelsCode1
Learning Differential Equations that are Easy to SolveCode1
Long Short-Term Memory Spiking Networks and Their ApplicationsCode1
Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks—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
Accuracy of neural networks for the simulation of chaotic dynamics: precision of training data vs precision of the algorithm—0
Few-Shot One-Class Classification via Meta-LearningCode1
Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands—0
MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activityCode1
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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