Discovering long term dependencies in noisy time series data using deep\n learning

2020/11/15 by Alexey Kurochkin, Kurochkin, Alexey
Computer Science · Decision Sciences · #Advanced Statistical Process Monitoring #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stock Market Forecasting Methods #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.2011.07551

openalex publication_date 2020/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Time series modelling is essential for solving tasks such as predictive\nmaintenance, quality control and optimisation. Deep learning is widely used for\nsolving such problems. When managing complex manufacturing process with neural\nnetworks, engineers need to know why machine learning model made specific\ndecision and what are possible outcomes of following model recommendation. In\nthis paper we develop framework for capturing and explaining temporal\ndependencies in time series data using deep neural networks and test it on\nvarious synthetic and real world datasets.\n

Citations

• Long Short-Term Memory
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
• Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Related

• Deep Learning for Time-Series Analysis
• seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention
• Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
• Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Data
• Recent Trends in Modelling the Continuous Time Series using Deep Learning: A Survey