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 1576–1600 of 6748 papers

TitleStatusHype
Transfer Learning for Autonomous Chatter Detection in Machining—0
Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC AlgorithmCode1
Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR—0
Configuration and Collection Factors for Side-Channel Disassembly—0
Meteorological indicators of dengue epidemics in non-endemic Northwest ArgentinaCode0
Multi-Label Clinical Time-Series Generation via Conditional GANCode1
On Principal Curve-Based Classifiers and Similarity-Based Selective Sampling in Time-Series—0
Prognostic classification based on random convolutional kernel—0
Transformer-Based Self-Supervised Learning for Emotion Recognition—0
Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products—0
Predicting Berth Stay for Tanker Terminals: A Systematic and Dynamic Approach—0
Forecasting new diseases in low-data settings using transfer learningCode0
Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version—0
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal frameworkCode0
The market drives ETFs or ETFs the market: causality without Granger—0
T4PdM: a Deep Neural Network based on the Transformer Architecture for Fault Diagnosis of Rotating Machinery—0
Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition—0
Binary Spatial Random Field Reconstruction from Non-Gaussian Inhomogeneous Time-series ObservationsCode0
Few-Shot Forecasting of Time-Series with Heterogeneous ChannelsCode1
Domain Adaptation for Time-Series Classification to Mitigate Covariate ShiftCode1
VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance Evaluated on Non-stationary Seismic Signal Time SeriesCode0
Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS—0
Attention-based CNN-LSTM and XGBoost hybrid model for stock predictionCode2
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding—0
Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning—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