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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 3526–3550 of 6748 papers

TitleStatusHype
Spectrum Attention Mechanism for Time Series Classification—0
Spectrum of non-Hermitian deep-Hebbian neural networks—0
Spurious memory in non-equilibrium stochastic models of imitative behavior—0
Spurious seasonality detection: a non-parametric test proposal—0
Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things—0
Squibs: Evaluation Methods for Statistically Dependent Text—0
SRDCNN: Strongly Regularized Deep Convolution Neural Network Architecture for Time-series Sensor Signal Classification Tasks—0
Non-destructive Fault Diagnosis of Electronic Interconnects by Learning Signal Patterns of Reflection Coefficient in the Frequency Domain—0
Stability Bounds for Non-i.i.d. Processes—0
Stability of China's Stock Market: Measure and Forecast by Ricci Curvature on Network—0
Stacked Boosters Network Architecture for Short Term Load Forecasting in Buildings—0
Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection—0
Stack Index Prediction Using Time-Series Analysis—0
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 1 Theory—0
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 2 Validation—0
Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series—0
State Drug Policy Effectiveness: Comparative Policy Analysis of Drug Overdose Mortality—0
State Duration and Interval Modeling in Hidden Semi-Markov Model for Sequential Data Analysis—0
State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems—0
State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting—0
Stationarity analysis of the stock market data and its application to mechanical trading—0
Stationarity of the detrended price return in stock markets—0
Stationarity of Time-Series on Graph via Bivariate Translation Invariance—0
Stationary Density Estimation of Itô Diffusions Using Deep Learning—0
Stationary Geometric Graphical Model Selection—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