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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 1901–1950 of 6748 papers

TitleStatusHype
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions—0
Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection—0
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
Defined the predictors of the lightning over India by using artificial neural network—0
A review of predictive uncertainty estimation with machine learning—0
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia—0
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data—0
Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression—0
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
Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases—0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
A Review of Open Source Software Tools for Time Series Analysis—0
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders—0
DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization—0
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling—0
Comparison of PCA with ICA from data distribution perspective—0
Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective—0
A Review of Mathematical and Computational Methods in Cancer Dynamics—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
Comparison of Machine Learning Methods for Predicting Karst Spring Discharge in North China—0
ANFIS-based prediction of power generation for combined cycle power plant—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
Bandwidth-efficient distributed neural network architectures with application to body sensor networks—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
Comparison of LSTM autoencoder based deep learning enabled Bayesian inference using two time series reconstruction approaches—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
American Hate Crime Trends Prediction with Event Extraction—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