Time Series Prediction and Neural Networks

2018

IoT services hosted by low-power devices rely on the cloud infrastructure to propagate their ubiquitous presence over the Internet. A critical challenge for IoT systems is to ensure continuous provisioning of IoT services by overcoming network breakdowns, hardware failures, and energy constraints. To overcome these issues, we propose a cloud-based framework namely SensorClone, which relies on virtual devices to improve IoT resilience. A virtual device is the digital counterpart of a physical device that has learned to emulate its operations from sample data collected from the physical one. SensorClone exploits the collected data of low-power devices to create virtual devices in the cloud. SensorClone then can opportunistically migrate virtual devices from the cloud into other devices, potentially underutilized, with higher capabilities and closer to the edge of the network, e.g., smart devices. Through a real deployment of our SensorClone in the wild, we identify that virtual device...

Nature Geoscience

Proceedings of the Sixteenth European Conference on Computer Systems

We can increase the efficiency of public cloud datacenters by harvesting allocated but temporarily idling CPU cores from customer virtual machines (VMs) to run batch or analytics workloads. Even small efficiency gains translate into substantial savings, since provisioning and operating a datacenter costs hundreds of millions of dollars per year. The main challenge is to harvest idle cores with little or no impact on customer VMs, which could be running latency-sensitive services and are essentially black-boxes to the cloud provider. We introduce ElasticVM, a new VM type that can run batch workloads cheaply using mainly harvested cores. We also propose SmartHarvest, a system that dynamically manages the number of cores available to ElasticVMs in each fine-grained time window. SmartHarvest uses online learning to predict the core demand of primary, customer VMs and compute the number of cores that can be safely harvested. Our results show that SmartHarvest can harvest a significant amount of CPU resources without increasing the 99th-percentile tail latency of latency-critical primary workloads by more than 10%. Unlike static harvesting techniques

European Journal of Science and Technology

Öz Türkiye regüle elektrik piyasasında, gün içi piyasasının ticaret hacmi gün geçtikçe artmaktadır. Bu durum piyasa katılımcıları için yüksek doğrulukta tahmin yapabilmeyi önemli hale getirmektedir. Gün içi piyasasında sürekli müzayede şeklinde alışveriş yapılmaktadır. Bu çalışmada, öncelikle Türkiye Gün İçi Piyasası elektrik fiyatlarının saatlik ağırlıklı ortalamaları alınarak, veri tahmin problemine hazır hale getirilmiştir. Piyasada işlem yapılmayan saatler bulunduğundan, eksik veri problemi ile karşılaşılmıştır. Elektrik fiyat tahmin literatüründe bu problemin çözümüne yönelik bir çalışma bulunmamaktadır. Bu çalışmada fiyat tahmini için kullanılacak olan eğitim verilerindeki eksiklerin nasıl tamamlanacağı üzerinde durulmuştur. Eksik veri tamamlama yöntemleri uygulamaları karşılaştırılmış, tek değişkenli Lasso yöntemi ile tahminler yapılarak, sonuçlar raporlanmıştır. Eksik verileri tamamlayarak tahmin yapmanın sonuca istatistiksel olarak anlamlı şekilde katkısı olmuştur. Sonuçlarımız, eksik verileri Gün Öncesi Piyasası değerleri ile tamamlamanın en başarılı yöntem olduğunu göstermiştir.

Computational Intelligence and Neuroscience, 2021

The rapid emergence of the novel SARS-CoV-2 poses a challenge and has attracted worldwide attention. Artificial intelligence (AI) can be used to combat this pandemic and control the spread of the virus. In particular, deep learning-based time-series techniques are used to predict worldwide COVID-19 cases for short-term and medium-term dependencies using adaptive learning. This study aimed to predict daily COVID-19 cases and investigate the critical factors that increase the transmission rate of this outbreak by examining different influential factors. Furthermore, the study analyzed the effectiveness of COVID-19 prevention measures. A fully connected deep neural network, long short-term memory (LSTM), and transformer model were used as the AI models for the prediction of new COVID-19 cases. Initially, data preprocessing and feature extraction were performed using COVID-19 datasets from Saudi Arabia. The performance metrics for all models were computed, and the results were subjected...

AIMS Bioengineering, 2021

This article focuses on the application of deep learning and spectral analysis to epidemiology time series data, which has recently piqued the interest of some researchers. The COVID-19 virus is still mutating, particularly the delta and omicron variants, which are known for their high level of contagiousness, but policymakers and governments are resolute in combating the pandemic's spread through a recent massive vaccination campaign of their population. We used extreme machine learning (ELM), multilayer perceptron (MLP), long short-term neural network (LSTM), gated recurrent unit (GRU), convolution neural network (CNN) and deep neural network (DNN) methods on time series data from the start of the pandemic in France, Russia, Turkey, India, United states of America (USA), Brazil and United Kingdom (UK) until September 3, 2021 to predict the daily new cases and daily deaths at different waves of the pandemic in countries considered while using root mean square error (RMSE) and r...

Journal of Physics: Conference Series, 2018

The attitude that this new man maintains facing nature will be radically different from the attitude he maintained in previous times. .

Annals of the New York Academy of Sciences, 2016

Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field.

Redmarka. Revista de Marketing Aplicado

La gestión del inventario de medicamentos es una de las tareas más complejas a realizar en una farmacia. Una buena estimación de las compras favorece el compromiso entre satisfacer la demanda de los usuarios y minimizar los costos de mantenimiento de inventario y de almacenamiento. Por ello, conocer a priori la demanda de un determinado medicamento ayuda a decidir qué cantidad se debe comprar de producto. Las aplicaciones inteligentes, como los sistemas de recomendaciones o los sistemas predictivos, son altamente demandados por la industria farmacéutica dado su potencial para optimizar la compra y/o tener un mayor control de los inventarios, entre otros beneficios. En este trabajo se proponen dos métodos para predecir la demanda de medicamentos de la Farmacia del Instituto del Seguro Social de Ecuador, en la ciudad de Ibarra; uno basado en series de tiempo y otro usando redes neuronales. Los métodos fueron aplicados a medicamentos que tenían un comportamiento estacional y cíclico. L...

Neural Computing and Applications, 2018

Engineering systems experiencing events of amplitudes higher than 100 g n for a duration under 100 ms, here termed high-rate dynamics, can undergo rapid damaging effects. If the structural health of such systems could be accurately estimated in a timely manner, preventative measures could be employed to minimize adverse effects. For complex high-rate problems, adaptive observers have shown promise due to their capability to deal with nonstationary, noisy, and uncertain systems. However, adaptive observers have slow convergence rates, which impede their applicability to the high-rate problems. To improve on the convergence rate, we propose a variable input space concept for optimizing the use of data history of high-rate dynamics, with the objective to produce an optimal representation of the system of interest. Using the embedding theory, the algorithm sequentially selects and adapts a vector of inputs that preserves the essential dynamics of the high-rate system. In this paper, the variable input space is integrated in a wavelet neural network, which constitutes a variable input observer. The observer is simulated using experimental data from a high-rate system. Different input space adaptation methods are studied, and the performance is also compared against an optimized fixed input strategy. It is found that a smooth transition of the input space eliminates error spikes and yields faster convergence. The variable input observer is further studied in a hybrid model-/data-driven formulation, and results demonstrate significant improvement in performance gained from the added physical knowledge.

Computational Mechanics

This work presents a multi-resolution physics-informed recurrent neural network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter identification of the MSK systems. The MSK application was selected as the model problem due to its challenging nature in mapping the high-frequency surface electromyography (sEMG) signals to the low-frequency body joint motion controlled by the MSK and muscle contraction dynamics. The proposed method utilizes the fast wavelet transform to decompose the mixed frequency input sEMG and output joint motion signals into nested multi-resolution signals. The prediction model is subsequently trained on coarser-scale input–output signals using a gated recurrent unit (GRU), and then the trained parameters are transferred to the next level of training with finer-scale signals. These training processes are repeated recursively under a transfer-learning fashion until the full-scale training (i.e., with unfiltered signals) is achie...