Time Series Analysis and Forecasting

Mercu Buana University Campus D is part of Mercu Buana University which began the operational in 2013. Since 2013 until 2017, Mercu Buana University Campus D still got less than a target about getting the new student.This can be due to various things including the lack of precisely marketing strategy undertaken.Therefore, in this study the authors make an application by implementing the concept of data mining using clustering and forecasting methods to obtain information from existing data registrants. So, the information can be used by decision makers to determine effective and efficient marketing strategies.

The paper addresses a decision support system for forecasting a total cargo throughput in the Port of Koper. The system is based on the combination of dynamic factor model (DFM) and ARIMAX time series model. The DFM extracts useful information obtained from the exogenous macroeconomic indicators. The dynamic factors are afterward directed into the ARIMAX forecasting model, which predicts a future behavior of the total cargo. The modeling design that consists of four stages has been used to obtain the final selected DFM-ARIMAX model. The results show that the forecasting system can predict well the observed throughput. Thus, it might serve as a useful reinforcement of an existing decision support system.

The autoregressive integrated moving average (ARIMA) model is extensively used in the fields of economics and finance for forecasting stock prices. Using vast amounts of historical data, it is used in the Philippine Stock Exchange (PSE) to forecast future stock price movements. In this paper, two new forecasting techniques are introduced: hidden markov models (HMM) and support vector regression with firefly algorithm (SVR-FA). Both methods are compared to ARIMA in analyzing closing stock prices of five selected Philippine companies: SM, Ayala Corporations (AC), Philippine Long Distance Telephone Company (TEL), JG Summit (JGS), and Manila Electric Railroad and Light Company (MER). All five companies present closing stock price movements that present a challenge to the proposed models as well as for ARIMA. The HMMs are trained using observable states that can emit possible movement of hidden states. In stock price forecasting, we may assume an underlying hidden movement that governs the actual increases (decreases) in stock prices for model estimation. The SVR-FA model uses the ε-sensitive loss function and a kernel function for analyzing the best regression hyperplane that relates the current day's closing stock prices with the future closing stock prices. Results show that SVRFA and HMM performed better than ARIMA in forecasting the closing stock prices ofthe selected companies with SVR-FA yielding forecasts with the lowest mean absolutepercentage errors (MAPEs) and mean absolute deviations (MADs).

Turkey is located in the Mediterranean Basin. The natural environment characteristics of the Mediterranean basin reflect human life and have resulted in economic activities specific to the region. The most important economic sector of the Mediterranean basin is agriculture. It currently provides employment for nearly half of the labour force and it uses most of the land and water resources (Makhzoumi and Pungetti 1999). The olive tree, which is the indicator tree of Mediterranean climate area, has an important role in Mediterranean culture and the livelihood of individuals (Atalay 2004). In the Mediterranean, olive business generates direct income for about 7 million families and indirectly supports 30–35 million families in less favoured areas (Fleskens 2007). One of the distinctive characteristics of the Mediterranean Basin is its climate conditions. The characteristic warm to hot dry summer and mild cool wet winter is distinguished by local variations determined by topography, aspect and vegetation cover. The majority of Mediterranean lands have a long dry season, which results in a characteristic scarcity of water resources. The Mediterranean is also referred to as the “olive climate” because of wide distribution of the cultivated olive tree. The cultivation of olive trees in the Mediterranean basin goes back to ancient times. The olive is a significant plant associated with the Mediterranean (Makhzoumi and Pungetti 1999). On the earth, the olive zone is most intense in the Mediterranean basin and has a great importance in the economic, social, and cultural life of these countries (Loumou ve Giourga 2003). The Mediterranean basin is one of the well-marked biogeographic regions (Atalay 2008). The olive tree is an element of maquis communities which occurs in the natural vegetation of Mediterranean climate (Efe et al. 2009). The olive, Olea europaea, is the only species of the botanical family Oleaceae with edible fruit (Rojas and Mosquera 2008). “Olea” is a wild fruit, and includes different species and sub-species. “Olea europeae” is one of these species and has a primary importance. Olea europeae has two main sub-species: “Olea europaea var. sylvestris” (wild olive) and “Olea europaea var. sativa” (cultivated olive) (Ünsal 2003). Its cultivation began more than 6000 years ago (making it one of the oldest cultivated plants) in the Middle East, and spread west on both sides of the Mediterranean Sea (Rojas and Mosquera 2008). The olive tree, which was grown naturally in areas of Mediterranean climate, is known to have been grown in these areas for different purposes for a long period of time (Terral 2000).

Purpose – The aim of this case study-based paper is to study the application of Six Sigma,
a breakthrough improvement strategy in the field of cell site construction of a telecom company.
Design/methodology/approach – This research provides action research of a Six Sigma project using DMAIC methodology carried out in cell site construction function of a telecom company. The research illustrates how the various Six Sigma tools and techniques were applied in a mutually inclusive manner in one project. The infrastructure department of the company had constructed 900 cell sites last year, out of which 150 were not according to standards and were either disapproved by the operations department or underwent maintenance soon after their use. In 2010, the company spent US$ 0.5 million on rework and maintenance at these sites, thus highlighting the urgency of the problem.
Findings – The paper shows how, after the implementation of the Six Sigma project, the company made savings worth US$ 0.45 million.
Originality/value – Six Sigma as a means of waste reduction has gained popularity among
researchers and practitioners. The literature on the methodology of Six Sigma and the management approach towards Six Sigma is burgeoning. While various Six Sigma tools and techniques and their application are discussed in literature independent of each other, the need has arisen to observe their systematic application as they apply in a project; every company can use this breakthrough improvement strategy to improve its processes by reducing waste and deriving the financial benefits.

Electricity is a significant form of energy that cannot be stored physically and is usually generated as
needed. In most research studies, the main aim is to ensure that sufficient electricity is generated to meet
future needs. In order to avoid waste or shortage, a good system needs to be designed to constantly
maintain the level of electricity needed. It is necessary to estimate independent factors because future
electricity volume is based not only on current net consumption but also on independent factors. In this
paper, a new framework is proposed to first estimate future independent factors using SARIMA (seasonal
auto-regressive iterative moving average) method and NARANN (nonlinear autoregressive artificial
neural network) method, both of which are called a ”forecasted scenario approach”. Subsequently, based
on these scenarios, a LADES (LASSO-based adaptive evolutionary simulated annealing) model and a
RADES (ridge-based adaptive evolutionary simulated annealing) model are applied to forecast the future
NEC (net electricity consumption). The proposed approaches are then validated with a case study in
Turkey. The experimental results show that our approach outperforms others when compared to previous
approaches. Finally, the results show that the NEC can be modeled, and it can be used to predict the
future NEC.

Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series are available, conditioning the forecast of an individual time series on past behaviour of similar, related time series can be beneficial. Given that the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model. We achieve this by globally training a Long Short-Term Memory network (LSTM) that exploits the nonlinear demand relationships available in an E-commerce product assortment hierarchy. Aside from the forecasting engine, we propose a systematic pre-processing framework to overcome the challenges in an E-commerce setting. We also introduce several product grouping strategies to supplement the LSTM learning schemes, in situations where sales patterns in a product portfolio are disparate. We empirically evaluate the proposed forecasting framework on a real-world online marketplace dataset from Walmart. com. Our method achieves competitive results on category level and super-departmental level datasets, outperforming state-of-the-art techniques