Trend Projection and Time Series Methods

Profillidis, Vassilios; Botzoris, George

doi:10.1016/b978-0-12-811513-8.00006-6

Cited by 21 publications

(14 citation statements)

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“…Time series can be defined as a series of data points recorded and analyzed in a time order, it is a sequence taken at equally spaced time periods [21]. The only independent variable in time series methods is time.…”

Section: Proposed Prediction Methodsmentioning

confidence: 99%

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Comparative analysis of short-term demand predicting models using ARIMA and deep learning

Bousqaoui

Slimani²,

Achchab

2021

IJECE

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The forecasting consists of taking historical data as inputs then using them to predict future observations, thus determining future trends. Demand prediction is a crucial component in the supply chain’s process that allows each member to enhance its performance and its profit. Nevertheless, because of demand uncertainty supply chains usually suffer from many problems such as the bullwhip effect. As a solution to those logistics issues, this paper presents a comparative analysis of four time series demand forecasting models; namely, the autoregressive integrated moving Average (ARIMA) a statistical model, the multi-layer perceptron (MLP) a feedforward neural network, the long short-term memory model (LSTM) a recurrent neural network and the convolutional neural network (CNN or ConvNet) a deep learning model. The experimentations are carried out using a real-life dataset provided by a supermarket in Morocco. The results clearly show that the convolutional neural network gives slightly better forecasting results than the Long short-term memory network.

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Section: Proposed Prediction Methodsmentioning

confidence: 99%

“…ARIMA is a mixed model that combines both the differenced autoregressive and moving average models. The final form of a time series model, which depends on its own p past values and on the q past values of white noise error terms [21], is as:…”

Section: Moving Average Modelmentioning

confidence: 99%

Comparative analysis of short-term demand predicting models using ARIMA and deep learning

Bousqaoui

Slimani²,

Achchab

2021

IJECE

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“…Auto-regressive integrated moving average (ARIMA) is a widely-used method for predicting the traffic flow, and on its basis, seasonal ARIMA (SARIMA) is particularly useful to model the seasonal traffic behavior [10]. In this work, we use SARIMA to predict the macroscopic traffic flow (i.e., the number of vehicles in each region) with minimum Akaike information criterion (AIC) [11].…”

Section: B Traffic Flow Prediction and User Task Predictionmentioning

confidence: 99%

On-demand Vehicular Fog Computing for Beyond 5G Networks

Mao¹,

Akgül²,

Cho³

et al. 2023

Preprint

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Emerging compute-intensive and latency-sensitive vehicular applications are expected to be deployed at the edge instead of the cloud to shorten the network latency. Mobile fog nodes carried by moving vehicles have been proposed to complement the stationary fog nodes co-located with base stations to handle the spatio-temporal variations of the demand in a cost-efficient way. Existing works on capacity planning for such vehicular fog computing (VFC) scenarios assume that the vehicular traffic follows certain spatio-temporal patterns, which may change in different seasons, and create capacity plans accordingly. In other words, they consider long-term capacity planning, leaving the adaptation to temporary changes or unexpected variations out of scope. In this work, we propose an integer linear programming (ILP) based framework to optimize the routing strategy of vehicular fog nodes (VFNs) in order to maximize the profit received by the service provider, taking into account the quality of service (QoS) received by the users and service level agreement (SLA) of various applications. To adapt to the temporal variations in demand, we predict the traffic flow and resource consumption from the users with feedback from service evaluation. To reduce the computational time and enable parallel processing, we create the capacity plan in two steps, namely global planning and regional planning. Through simulations, we show that the proposed solution achieves an 85% higher profit and a 20% higher service rate compared to the strategy where the VFNs randomly travel and serve the surrounding users without demand prediction. It achieves similar network latency compared to the strategy using only stationary fog nodes, but with a higher cost-efficiency. We also evaluate the impacts of number of VFNs, cost parameters, and regional size on the capacity plan. We find that a high number of VFNs, a small regional size, a high penalty cost, and low traveling and rental costs will lead to a high service rate; while a large regional size and low traveling, rental, and penalty costs will result in a high profit.

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“…The appropriate lag length in the ARDL model was selected through the Akaike information criterion (AIC). The AIC is considered to be a useful model (Burnham & Anderson, 2004;Profillidis & Botzoris, 2018), so it was employed to determine the ideal lag length incorporated in the model. Cointegration and the error correction econometric method were employed for the estimation of the stated models.…”

Section: Autoregressive Distributed Lag Modelmentioning

confidence: 99%

Dilemma of Natural Resource Abundance: A Case Study of Kuwait

Aljarallah

Angus

2020

SAGE Open

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There is a lively debate about the relationship between a nation’s natural resource abundance and economic growth. Some view natural resource abundance as a curse, whereas others view it as a blessing. This study examines the economic, social, and political effects of resource abundance in an oil-rich country, Kuwait, using data from 1984 to 2014. This study analyzes the short- and long-run impacts of resource rents on per capita gross domestic product (GDP), productivity, human capital, and institutional quality. The study reveals through autoregressive distributed lag modeling and error correction modeling that resource rents increase per capita GDP merely in the short-run; however, resource rents deteriorate productivity, human capital, and institutional quality in both the short and the long-run. These results indicate that, for Kuwait, the overreliance on its natural resources has been detrimental over the long-run. The study suggests that there is a need to improve the quality of institutions and enhance the level of human capital to get economic sustainability and development over time.

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Trend Projection and Time Series Methods

Cited by 21 publications

References 0 publications

Comparative analysis of short-term demand predicting models using ARIMA and deep learning

Comparative analysis of short-term demand predicting models using ARIMA and deep learning

On-demand Vehicular Fog Computing for Beyond 5G Networks

Dilemma of Natural Resource Abundance: A Case Study of Kuwait

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