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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

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Showing 4451–4500 of 6748 papers

TitleStatusHype
Joint Characterization of Multiscale Information in High Dimensional Data—0
Joint community and anomaly tracking in dynamic networks—0
Joint Estimation of Multiple Graphical Models from High Dimensional Time Series—0
Joint Forecasting and Interpolation of Graph Signals Using Deep Learning—0
Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI—0
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks—0
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values—0
Joint modeling of multiple time series via the beta process with application to motion capture segmentation—0
Joint Multi-Dimensional Model for Global and Time-Series Annotations—0
Joint multifractal analysis based on the partition function approach: Analytical analysis, numerical simulation and empirical application—0
Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting—0
Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes—0
Joint Normality Test Via Two-Dimensional Projection—0
Joint segmentation of multivariate time series with hidden process regression for human activity recognition—0
Joint Time-Vertex Fractional Fourier Transform—0
Jumpy Recurrent Neural Networks—0
K-ARMA Models for Clustering Time Series Data—0
KDCTime: Knowledge Distillation with Calibration on InceptionTime for Time-series Classification—0
KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution—0
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting—0
Kernel Density Estimation-Based Markov Models with Hidden State—0
Kernel distance measures for time series, random fields and other structured data—0
Kernel Estimation for Panel Data with Heterogeneous Dynamics—0
Kernel Least Mean Square with Adaptive Kernel Size—0
Kernel Methods for Nonlinear Connectivity Detection—0
Kernels for sequentially ordered data—0
Kernels for time series with irregularly-spaced multivariate observations—0
Kernel Spectral Clustering and applications—0
Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach—0
Ketamine-Medetomidine General Anesthesia Occurs With Alternation of Cortical Electrophysiological Activity Between High and Low Complex States—0
Kinetic Compressive Sensing—0
Kinetic theory and Brazilian income distribution—0
KINN: Incorporating Expert Knowledge in Neural Networks—0
KnitCity: a machine learning-based, game-theoretical framework for prediction assessment and seismic risk policy design—0
Knock Detection in Combustion Engine Time Series Using a Theory-Guided 1D Convolutional Neural Network Approach—0
Knowledge Extracted from Recurrent Deep Belief Network for Real Time Deterministic Control—0
Kolmogorov-Arnold Networks (KANs) for Time Series Analysis—0
Kolmogorov Space in Time Series Data—0
Koopman Mode Decomposition of Oscillatory Temperature Field inside a Room—0
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts—0
KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios—0
Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning—0
Label-Efficient Interactive Time-Series Anomaly Detection—0
Label-efficient Time Series Representation Learning: A Review—0
Lagged and instantaneous dynamical influences related to brain structural connectivity—0
Lagged correlation-based deep learning for directional trend change prediction in financial time series—0
Lagrange regularisation approach to compare nested data sets and determine objectively financial bubbles' inceptions—0
Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming—0
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks—0
Land use/land cover dynamics on vulnerable regions in Uruguay approached by a method combining Maximum Entropy and Population Dynamics—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