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 2601–2625 of 6748 papers

TitleStatusHype
Fast Partial Fourier Transform—0
Fast Robust Methods for Singular State-Space Models—0
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks—0
Fast-Slow Streamflow Model Using Mass-Conserving LSTM—0
Fast Stability Scanning for Future Grid Scenario Analysis—0
Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images—0
A novel method of fuzzy time series forecasting based on interval index number and membership value using support vector machine—0
Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network—0
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Fast Variational Inference for Large-scale Internet Diagnosis—0
Ensemble Committees for Stock Return Classification and Prediction—0
Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection—0
Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Unsupervised Learning—0
Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems—0
A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data—0
Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models—0
Bayesian forecast combination using time-varying features—0
Feature-based time-series analysis—0
Forecasting Method for Grouped Time Series with the Use of k-Means Algorithm—0
Feature Construction and Selection for PV Solar Power Modeling—0
Approximation algorithms for confidence bands for time series—0
Feature Importance for Time Series Data: Improving KernelSHAP—0
Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF)—0
Approximation Theory of Convolutional Architectures for Time Series Modelling—0
Forecasting in multivariate irregularly sampled time series with missing values—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