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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 5151–5200 of 6748 papers

TitleStatusHype
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets—0
Anomaly Detection and Localization based on Double Kernelized Scoring and Matrix Kernels—0
Anomaly detection and motif discovery in symbolic representations of time series—0
Anomaly detection and regime searching in fitness-tracker data—0
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes—0
Anomaly Detection and Sampling Cost Control via Hierarchical GANs—0
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models—0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning—0
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning—0
Anomaly Detection for Fraud in Cryptocurrency Time Series—0
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle—0
Anomaly Detection for Industrial Big Data—0
Anomaly Detection for Multivariate Time Series of Exotic Supernovae—0
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder—0
Anomaly Detection in Cloud Components—0
Anomaly detection in dynamical systems from measured time series—0
Anomaly Detection in Intra-Vehicle Networks—0
Anomaly detection in laser-guided vehicles' batteries: a case study—0
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms—0
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning—0
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art—0
Anomaly Detection Models for IoT Time Series Data—0
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder—0
Anomaly Detection on Graph Time Series—0
Anomaly Detection on IT Operation Series via Online Matrix Profile—0
Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems—0
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data—0
Anomaly Detection with HMM Gauge Likelihood Analysis—0
Anomaly detection with Wasserstein GAN—0
Anomaly Subsequence Detection with Dynamic Local Density for Time Series—0
A Non-linear Function-on-Function Model for Regression with Time Series Data—0
An OPC UA-based industrial Big Data architecture—0
An Optimally Weighted Echo State Neural Network for Highly Chaotic Time Series Modelling—0
An Optimal Reduction of TV-Denoising to Adaptive Online Learning—0
An Optimized and Energy-Efficient Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks—0
An Ordered Lasso and Sparse Time-Lagged Regression—0
An Ordinal Pattern Approach to Detect and to Model Leverage Effects and Dependence Structures Between Financial Time Series—0
A Novel Algorithm for the Maximal Fit Problem in Boolean Networks—0
A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network—0
A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators—0
A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes—0
A novel approach to quantify volatility prediction—0
A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power System—0
A Novel Cluster Classify Regress Model Predictive Controller Formulation; CCR-MPC—0
A Novel CNN-LSTM-based Approach to Predict Urban Expansion—0
A novel convolutional neural network model to remove muscle artifacts from EEG—0
A Novel Deep Parallel Time-series Relation Network for Fault Diagnosis—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks—0
Phase-randomised Fourier transform model for the generation of synthetic wind speeds—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