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

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Showing 4351–4400 of 6748 papers

TitleStatusHype
Prediction with Spatio-temporal Point Processes with Self Organizing Decision Trees—0
What went wrong and when? Instance-wise Feature Importance for Time-series Models—0
DANTE: A framework for mining and monitoring darknet traffic—0
Optimally adaptive Bayesian spectral density estimation for stationary and nonstationary processes—0
Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning—0
Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural NetworksCode0
Adaptive exponential power distribution with moving estimator for nonstationary time series—0
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution—0
Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study—0
Learning to Generate Time Series Conditioned Graphs with Generative Adversarial Nets—0
Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time SeriesCode0
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences—0
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network—0
Identity Recognition in Intelligent Cars with Behavioral Data and LSTM-ResNet Classifier—0
The statistical physics of discovering exogenous and endogenous factors in a chain of events—0
Online Hierarchical Forecasting for Power Consumption Data—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
Forecasting Models for Daily Natural Gas Consumption Considering Periodic Variations and Demand Segregation—0
Wind Speed Prediction using Deep Ensemble Learning with a Jet-like Architecture—0
DROCC: Deep Robust One-Class Classification—0
Time Series Data Augmentation for Deep Learning: A Survey—0
A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs—0
How Much Can A Retailer Sell? Sales Forecasting on Tmall—0
Prediction of adverse events in Afghanistan: regression analysis of time series data grouped not by geographic dependencies—0
Complexity Measures and Features for Times Series classification—0
Evaluation of Temporal Complexity Reduction Techniques Applied to Storage Expansion Planning in Power System Models—0
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks—0
Geometric Fusion via Joint Delay Embeddings—0
Multivariate time-series modeling with generative neural networks—0
Block Hankel Tensor ARIMA for Multiple Short Time Series ForecastingCode0
Variational Hyper RNN for Sequence Modeling—0
Adaptive Transmit Waveform Design using Multi-Tone Sinusoidal Frequency Modulation—0
A New Unified Deep Learning Approach with Decomposition-Reconstruction-Ensemble Framework for Time Series Forecasting—0
Longitudinal Support Vector Machines for High Dimensional Time Series—0
Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural NetworksCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks—0
Forecasting Realized Volatility Matrix With Copula-Based Models—0
SummerTime: Variable-length Time SeriesSummarization with Applications to PhysicalActivity Analysis—0
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting—0
Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case—0
Dynamic Graph Learning based on Graph Laplacian—0
Controlled time series generation for automotive software-in-the-loop testing using GANs—0
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition—0
Fully convolutional networks for structural health monitoring through multivariate time series classification—0
Online Learning of the Kalman Filter with Logarithmic Regret—0
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals—0
Gaussian process imputation of multiple financial series—0
Exact Indexing of Time Series under Dynamic Time Warping—0
On the statistics of scaling exponents and the Multiscaling Value at Risk—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