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 1926–1950 of 6748 papers

TitleStatusHype
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