Browsing by Subject "Time series forecasting"

Now showing 1 - 2 of 2

Open Access
Collective data forecasting in dynamic transport networks
(2021-09) Güvercin, Mehmet
Forecasting is a crucial tool for intelligent transportation systems and passengers of these systems and critical for transportation planning and management, as the transportation variable (e.g. delay, traﬃc speed) are among major costs in transportation. Each transportation variable may cause a further propagation in dynamic transport network. Hence, the transportation variable pattern of a node and the location of the node in the transport network can provide useful information for other nodes. We address the problem of forecasting transportation variable of a transport network node, utilizing the network information as well as the transportation variable patterns of similar nodes in the network. We propose ECFM, Exploratory Clustered Forecasting Modeling, on both static and dynamic transportation network which makes use of graph based fea-tures for time-series estimation. ECFM approach builds a representative time-series for each group of nodes in the transport network and ﬁts a common model like Seasonal Autoregressive Integrated Moving Average (SARIMA), Long-Short Term Memory (LSTM), Regression with Autoregressive Integrated Moving Av-erage errors (REG-ARIMA), Regression with Long-Short Term Memory errors (REG-LSTM) for each, using the network based features as regressors. The models are then applied individually to each node data for predicting the node’s transportation variable. We perform a network based analysis of the transport network and identify graph-based features and we represent nodes as vectors that are used for both grouping nodes and as regressors in forecasting models. We evaluate proposed ECFM, Exploratory Clustered Forecasting Modeling, on two datasets (ﬂight de-lay dataset, traﬃc speed dataset). The experiments show that ECFM provides accurate forecasts of delays/traﬃcs compared to individual forecasting models. Centrality measure of nodes such as betweenness centrality score is found to be an eﬀective regressor in the clustered modeling. Clustered models built on dynamic networks performs better compared to static networks. ECFM, Exploratory Clustered Forecasting Modeling, is an conceptual ap-proach and it is domain independent. Our proposed approach tries to incorporate information, related to estimated variable, exist in similar nodes of the network. Thus, we can achieve to build robust estimation models on enriched data.
Open Access
A comparative study of deep learning architectures for multivariate cloud workload prediction
(2022-06) Gözen, Derya
Cloud computing and use of cloud data centers are in high demand due to their benefits to customers including but not limited to low cost, high availability, reliability, robustness and scalability. Cloud service providers are obliged to fulfill service level agreements that promise high quality of service to their customers. This brings out the need for effective and efficient utilization of data center resources, especially the resources of the compute servers. To achieve proactive and effective resource allocation and scaling policies, accurate prediction of workloads in cloud computing environments plays a critical role. Cloud workload prediction is a challenging task due to high dimensionality, variance and complexity of the workload data. In addition, workload prediction models are expected to work with sufficient amount of past observations to correctly learn workload patterns, at the same time, handle longer forecast horizons accurately. In order to tackle this problem and address the challenges, we investigated and compared five deep learning-based schemes for multivariate time series forecasting to predict the CPU utilization of virtual machines in cloud data centers. The performance of the deep learning schemes is analyzed and compared by using two real-world data sets: Alibaba cluster trace and Bitbrains trace. Our study reveals the relative strengths and weaknesses of the compared schemes for cloud workload prediction. We also observed that, among the compared schemes, Encoder-Decoder LSTM Network with Attention is a more effective solution for workload prediction in cloud computing.