Robust Online Time Series Prediction with Recurrent Neural Networks

Time series forecasting for streaming data plays an important role in many real applications, ranging from IoT systems, cyber-networks, to industrial systems and healthcare. However the real data is often complicated with anomalies and change points, which can lead the learned models deviating from the underlying patterns of the time series, especially in the context of online learning mode. In this paper we present an adaptive gradient learning method for recurrent neural networks (RNN) to forecast streaming time series in the presence of anomalies and change points. We explore the local features of time series to automatically weight the gradients of the loss of the newly available observations with distributional properties of the data in real time. We perform extensive experimental analysis on both synthetic and real datasets to evaluate the performance of the proposed method

A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series

This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases. We combine a well-designed feature extractor with GAN to help train the whole network. Aimed at obtaining data distribution and hidden pattern in both original distinguishing features and latent space, the encoder-decoder-encoder three-sub-network is employed in GAN, based on Deep Convolution Generative Adversarial Networks (DCGAN) but without Tanh activation layer and only trained on normal samples. In order to verify the validity and feasibility of our approach, we test it on rolling bearing data from Case Western Reserve University and further verify it on data collected from our laboratory. The results show that our proposed approach can achieve excellent performance in detecting faulty by outputting much larger evaluation scores

Educational Bandwidth Traffic Prediction using Non-Linear Autoregressive Neural Networks

Time series network traffic analysis and forecasting are important for fundamental to many decision-making processes, also to understand network performance, reliability and security, as well as to identify potential problems. This paper provides the latest work at London South Bank University (LSBU) network data traffic analysis by adapting nonlinear autoregressive exogenous model (NARX) based on the Levenberg-Marquardt backpropagation algorithm. This technique can analyze and predict data usage in its current and future states, as well as visualise the hourly, daily, weekly, monthly, and quarterly activities with less computation requirement. Results and analysis proved the accuracy of the prediction techniques

Educational bandwidth traffic prediction using non-linear autoregressive neural networks

Time series network traffic analysis and forecasting are important for fundamental to many decision-making processes, also to understand network performance, reliability and security, as well as to identify potential problems. This paper provides the latest work on London South Bank University (LSBU) network data traffic analysis by adapting nonlinear autoregressive exogenous model (NARX) based on Levenberg-Marquardt backpropagation algorithm. This technique can analyse and predict data usage in its current and future states, as well as visualise the hourly, daily, weekly, monthly, and quarterly activities with less computation requirement. Results and analysis proved the accuracy of the prediction techniques

Combining long-short term memory and reinforcement learning for improved autonomous network operation

A combined LSTM and RL approach is proposed for dynamic connection capacity allocation. The LSTM predictor anticipates periodical long-term sharp traffic changes and extends short-term RL knowledge. Numerical results show remarkable performance.The research leading to these results has received funding from the Spanish MINECO TWINS project (TEC2017-90097-R) and by the ICREA institution.Peer ReviewedPostprint (published version

Distributed Time Series Analytics

In recent years time series data has become ubiquitous thanks to affordable sensors and advances in embedded technology. Large amount of time-series data are continuously produced in a wide spectrum of applications, such as sensor networks, medical monitoring and so on. Availability of such large scale time series data highlights the importance of of scalable data management, efï¬cient querying and analysis. Meanwhile, in the online setting time series carries invaluable information and knowledge about the real-time status of involved entities or monitored phenomena, which calls for online time series data mining for serving timely decision making or event detection. In this thesis we aim to address these important issues pertaining to scalable and distributed analytics techniques for massive time series data. Concretely, this thesis is centered around the following three topics: As the number of sensors that pervade our lives signiï¬cantly increases (e.g., environmental sensors, mobile phone sensors, IoT applications, etc.), the efï¬cient management of massive amount of time series from such sensors is becoming increasingly important. The inï¬nite nature of sensor data poses a serious challenge for query processing even in a cloud infrastructure. Traditional raw sensor data management systems based on relational databases lack scalability to accommodate large scale sensor data efï¬ciently. Thus, distributed key-value stores in the cloud are becoming a prime tool to manage sensor data. However, currently there are no techniques for indexing and/or query optimization of the model-view sensor time series data in the cloud. In Chapter 2, we propose an innovative index for modeled segments in key-value stores, namely KVI-index. KVI-index consists of two interval indices on the time and sensor value dimensions respectively, each of which has an in-memory search tree and a secondary list materialized in the key-value store. The dramatic increase in the availability of data streams fuels the development of many distributed real-time computation engines (e.g., Storm, Samza, Spark Streaming, S4 etc.). In Chapter 3, we focus on a fundamental time series mining task in such a new computation paradigm, namely continuously mining dynamic (lagged) correlations in time series via a distributed real-time computation engine. Correlations reveal the hidden and temporal interactions across time series and are widely used in scientiï¬c data analysis, data-driven event detection, ï¬nance markets and so on. We propose the P2H framework consisting of a parallelism-partitioning based data shufï¬ing and a hypercube structure based computation pruning method, so as to enhance both the communication and computation efï¬ciency for mining correlations in the distributed context. In numerous real-world applications large datasets collected from observations and measurements of physical entities are inevitably noisy and contain outliers. The outliers in such large and noisy datasets can dramatically degrade the performance of standard distributed machine learning approaches such as s regression trees. In Chapter 4 we present a novel distributed regression tree approach that utilizes robust regression statistics, statistics that are more robust to outliers, for handling large and noisy datasets. Then we present an adaptive gradient learning method for recurrent neural networks (RNN) to forecast streaming time series in the presence of both outliers and change points