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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 5001–5050 of 6748 papers

TitleStatusHype
Kernel Estimation for Panel Data with Heterogeneous Dynamics—0
TrendNets: Mapping Emerging Research Trends From Dynamic Co-Word Networks via Sparse Representation—0
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models—0
Topological Data Analysis of Time Series Data for B2B Customer Relationship Management—0
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided EmbeddingCode0
Type-Driven Automated Learning with LaleCode0
Generative adversarial network based on chaotic time series—0
Devil in the Detail: Attack Scenarios in Industrial Applications—0
Sequential Gaussian Processes for Online Learning of Nonstationary FunctionsCode0
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning—0
Style Transfer with Time Series: Generating Synthetic Financial Data—0
Predicting Sparse Clients' Actions with CPOPT-Net in the Banking EnvironmentCode0
Robust guarantees for learning an autoregressive filter—0
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs—0
Interpreting a Recurrent Neural Network's Predictions of ICU Mortality Risk—0
Fully Neural Network based Model for General Temporal Point ProcessesCode0
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs—0
CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series ImputationCode0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
Effect of shapes of activation functions on predictability in the echo state network—0
Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems—0
Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection—0
Topological Feature Vectors for Chatter Detection in Turning Processes—0
Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets—0
Learning Ensembles of Anomaly Detectors on Synthetic Data—0
Time Series Analysis and Forecasting of the US Housing Starts using Econometric and Machine Learning Model—0
On Selecting Stable Predictors in Time Series Models—0
Semantic Analysis of Traffic Camera Data: Topic Signal Extraction and Anomalous Event Detection—0
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning—0
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks—0
Predicting Solar Flares Using a Long Short-Term Memory NetworkCode0
Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series—0
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features—0
A self-organising eigenspace map for time series clustering—0
Online Anomaly Detection with Sparse Gaussian Processes—0
Aphids, Ants and Ladybirds: a mathematical model predicting their population dynamics—0
Similarity Grouping-Guided Neural Network Modeling for Maritime Time Series Prediction—0
A New Valuation Measure for the Stock MarketCode0
Long Short-Term Memory with Gate and State Level Fusion for Light Field-Based Face Recognition—0
Time-Series Event Prediction with Evolutionary State GraphCode0
Capturing Evolution Genes for Time Series Data—0
Inferring Global Dynamics of a Black-Box System Using Machine Learning—0
Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks—0
Differential Recurrent Neural Network and its Application for Human Activity Recognition—0
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series ForecastingCode0
Generalized Dilation Neural Networks—0
Unsupervised Learning through Temporal Smoothing and Entropy Maximization—0
Learning Causality: Synthesis of Large-Scale Causal Networks from High-Dimensional Time Series Data—0
Temporal Attention Augmented Bilinear Network for Financial Time Series Data AnalysisCode0
Multivariate Time Series Classification using Dilated Convolutional Neural NetworkCode0
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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