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 1151–1200 of 6748 papers

TitleStatusHype
Near Optimal Heteroscedastic Regression with Symbiotic Learning—0
Deep learning-based deconvolution for interferometric radio transient reconstructionCode0
Characterizing the Emotion Carriers of COVID-19 Misinformation and Their Impact on Vaccination Outcomes in India and the United States—0
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition—0
Deep Learning for Energy Time-Series Analysis and Forecasting—0
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning—0
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
Switching Autoregressive Low-rank Tensor ModelsCode0
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models—0
An OPC UA-based industrial Big Data architecture—0
Evaluating generation of chaotic time series by convolutional generative adversarial networksCode0
SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise—0
Sliding Window Sum Algorithms for Deep Neural Networks—0
Bias, Consistency, and Partisanship in U.S. Asylum Cases: A Machine Learning Analysis of Extraneous Factors in Immigration Court Decisions—0
Comfort Foods and Community Connectedness: Investigating Diet Change during COVID-19 Using YouTube Videos on Twitter—0
Predicting Unplanned Readmissions in the Intensive Care Unit: A Multimodality Evaluation—0
Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary PerspectiveCode0
Ordinal time series analysis with the R package otsfeatures—0
Non-destructive Fault Diagnosis of Electronic Interconnects by Learning Signal Patterns of Reflection Coefficient in the Frequency Domain—0
Filling out the missing gaps: Time Series Imputation with Semi-Supervised Learning—0
The short- and long-term determinants of fertility in Uruguay—0
Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning—0
Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data—0
Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning—0
FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting—0
Delay-SDE-net: A deep learning approach for time series modelling with memory and uncertainty estimatesCode0
Deep incremental learning models for financial temporal tabular datasets with distribution shifts—0
Sinkhorn-Flow: Predicting Probability Mass Flow in Dynamical Systems Using Optimal Transport—0
Hybrid Variational Autoencoder for Time Series Forecasting—0
Mobile Mapping Mesh Change Detection and Update—0
Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study—0
Towards Unsupervised Learning based Denoising of Cyber Physical System Data to Mitigate Security Concerns—0
A Fourier Transform Approach for Automatic Detection of Oysters Spawning—0
DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena—0
Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shiftCode0
Explainable AI for Time Series via Virtual Inspection Layers—0
Accurate Prediction of Global Mean Temperature through Data Transformation Techniques—0
A Novel Method Combines Moving Fronts, Data Decomposition and Deep Learning to Forecast Intricate Time Series—0
On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM SignalsCode0
Machine Learning Enhanced Hankel Dynamic-Mode Decomposition—0
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG SignalsCode0
Local-Global Methods for Generalised Solar Irradiance Forecasting—0
On the Soundness of XAI in Prognostics and Health Management (PHM)Code0
Multi-task Meta Label Correction for Time Series PredictionCode0
Learning Representation for Anomaly Detection of Vehicle Trajectories—0
Efficient Certified Training and Robustness Verification of Neural ODEsCode0
Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals—0
Distributional Vector Autoregression: Eliciting Macro and Financial Dependence—0
Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model—0
Multimodal Multi-User Surface Recognition with the Kernel Two-Sample TestCode0
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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