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 3301–3350 of 6748 papers

TitleStatusHype
A geometric analysis of nonlinear dynamics and its application to financial time series—0
COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms—0
Multi-Faceted Representation Learning with Hybrid Architecture for Time Series Classification—0
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior—0
Score Matched Neural Exponential Families for Likelihood-Free InferenceCode1
Random pattern and frequency generation using a photonic reservoir computer with output feedback—0
Technical Progress Analysis Using a Dynamic Topic Model for Technical Terms to Revise Patent Classification Codes—0
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
United States FDA drug approvals are persistent and polycyclic: Insights into economic cycles, innovation dynamics, and national policy—0
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting—0
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series ClassificationCode1
Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study—0
Using Spatio-temporal Deep Learning for Forecasting Demand and Supply-demand Gap in Ride-hailing System with Anonymised Spatial Adjacency Information—0
Detection of Anomalies in a Time Series Data using InfluxDB and Python—0
Proofs and additional experiments on Second order techniques for learning time-series with structural breaks—0
LSTM-based Space Occupancy Prediction towards Efficient Building Energy Management—0
Anomaly Detection and Localization based on Double Kernelized Scoring and Matrix Kernels—0
Informer: Beyond Efficient Transformer for Long Sequence Time-Series ForecastingCode1
Clustering high dimensional meteorological scenarios: results and performance index—0
fMRI-Kernel Regression: A Kernel-based Method for Pointwise Statistical Analysis of rs-fMRI for Population Studies—0
Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODECode0
Interpolation and Gap Filling of Landsat Reflectance Time Series—0
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation—0
Quantifying Synchronization in a Biologically Inspired Neural Network—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
A Review of Hidden Markov Models and Recurrent Neural Networks for Event Detection and Localization in Biomedical Signals—0
Intrinsic persistent homology via density-based metric learningCode1
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics—0
Building Deep Learning Models to Predict Mortality in ICU Patients—0
Online Joint Topology Identification and Signal Estimation from Streams with Missing Data—0
T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis—0
Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers—0
Estimation of Large Financial Covariances: A Cross-Validation Approach—0
A deep network approach to multitemporal cloud detection—0
Enhanced Recurrent Neural Tangent Kernels for Non-Time-Series DataCode0
Parametric measures of variability induced by risk measures—0
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Topology Identification under Spatially Correlated Noise—0
An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System—0
Automatic Registration and Clustering of Time Series—0
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks—0
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution RobustnessCode0
Physics-Aware Gaussian Processes in Remote Sensing—0
Randomized kernels for large scale Earth observation applications—0
An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth—0
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous SensorsCode0
A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network—0
Dynamic Clustering in Federated Learning—0
[Re] Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-AttentionCode0
Modified Auto Regressive Technique for Univariate Time Series Prediction of Solar Irradiance—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