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 6151–6200 of 6748 papers

TitleStatusHype
Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis—0
Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models—0
Demand Forecasting in Smart Grid Using Long Short-Term Memory—0
Demand Forecasting of Individual Probability Density Functions with Machine Learning—0
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity—0
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework—0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
Denoising diffusion probabilistic models for probabilistic energy forecasting—0
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders—0
Denoising neural networks for magnetic resonance spectroscopy—0
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks—0
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification—0
Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective—0
Dependent Matérn Processes for Multivariate Time Series—0
Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals—0
Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks—0
Deriving land surface phenology indicators from CO2 eddy covariance measurements—0
Characterizing the memory capacity of transmon qubit reservoirs—0
Design-time Fashion Popularity Forecasting in VR Environments—0
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data—0
Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data—0
Deteção de estruturas permanentes a partir de dados de séries temporais Sentinel 1 e 2—0
DETECT: A Hierarchical Clustering Algorithm for Behavioural Trends in Temporal Educational Data—0
Detecting and explaining changes in various assets' relationships in financial markets—0
Locating line and node disturbances in networks of diffusively coupled dynamical agents—0
Detecting and modelling delayed density-dependence in abundance time series of a small mammal (Didelphis aurita)—0
Detecting a trend change in cross-border epidemic transmission—0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Detecting CAN Masquerade Attacks with Signal Clustering Similarity—0
Detecting Change in Seasonal Pattern via Autoencoder and Temporal Regularization—0
Detecting changes in slope with an L_0 penalty—0
Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags—0
Detecting Concrete Abnormality Using Time-series Thermal Imaging and Supervised Learning—0
Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis—0
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network—0
Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model—0
Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Deep Learning-Based Time Series Forecasting—0
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving—0
Detecting Gas Vapor Leaks Using Uncalibrated Sensors—0
Detecting Handwritten Mathematical Terms with Sensor Based Data—0
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning—0
Detecting residues of cosmic events using residual neural network—0
Detecting Rough Volatility: A Filtering Approach—0
Detecting Slag Formations with Deep Convolutional Neural Networks—0
Detecting Structural Breaks in Foreign Exchange Markets by using the group LASSO technique—0
Detection of Anomalies in a Time Series Data using InfluxDB and Python—0
The amplitude modulation pattern of Gaussian noise is a fingerprint of Gaussianity—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