scholarly journals Peramalan Jumlah Penumpang Kapal Laut Menggunakan Metode Fuzzy Runtun Waktu Chen Orde Tinggi

2021 ◽  
pp. 38-48
Author(s):  
Rizki Adiputra ◽  
Erna Tri Herdiani ◽  
Sitti Sahriman
Keyword(s):  
Time Series ◽  
Second Order ◽  
High Order ◽  
Fuzzy Time Series ◽  
Future Period ◽  
Fuzzy Method ◽  
Testing Data ◽  
The Future ◽  
Operational Activities

The port has become an important part of people's lives. On certain days there is an increase in the number of ship passengers which can slow down operational activities from the port, thus causing a buildup of passengers at the port. therefore, the port must be prepared to deal with the buildup of passengers at the port. Based on this, the researchers made a prediction or forecasting the number of ship passengers at Makassar Soekarno Hatta Port in the coming period to find out how much the estimated number of passengers at Makassar Soekarno Hatta Port. The results of these studies can be input to the PT. Pelabuhan Indonesia IV (Persero ) Makassar if there will be a surge in passengers in the future period. researchers used the fuzzy method of high order chen time series in forecasting or prediction in this study . The researcher divides the data onto training and testing data . The results of the study using fuzzy time series with the best high order chen are that the second order produces MAPE error size of 0,143 , MSE 13470993,9 and MAE of 9478,52 . The result of prediction of testing data onto one period in the future is 52.608.

scholarly journals FDI Time Series Forecasts: Evidence from Emerging Markets

2021 ◽  
Author(s):  
Marina Dobrota ◽  
Nikola Zornić ◽  
Aleksandar Marković
Keyword(s):  
Time Series ◽  
Developing Countries ◽  
Economic Development ◽  
Emerging Markets ◽  
Research Question ◽  
Southeastern Europe ◽  
Western Balkans ◽  
Future Period ◽  
Fdi Inflows ◽  
The Future

Research Question: This paper investigates the trend and flow of foreign direct investments (FDI) in emerging markets, with the focus on FDI in Serbia in comparison with akin countries from the region. Motivation: FDI is an important factor of growth and prosperity in developing countries. It largely influences trade, productivity, and economic development of a receiving country. Based on UNCTAD’s World Investment Report of 2019, the share of global FDI in developing countries was 54 per cent, which was a record. Recently, Serbia has been recognized as one of the most popular destinations for FDI in Southeastern Europe. This motivated us to analyze the chances and possibilities of enlargement of FDI in Serbia, as well in other Balkan countries. Idea: The main idea of the paper is to analyze and estimate time series of FDI net inflows for Serbia. We strive to investigate whether FDI demonstrates the durable growth in the future period of time. Furthermore, we compare the state of Serbian FDI with the former Yugoslav countries, in search for disparities or similarities. Data: We observed the FDI net inflows that are measured in current US dollars, while the data were retrieved from the World Bank database. The earliest available time point is 1992, while the latest available year of observation is 2018. Tools: We estimated the FDI net flow time series using a list of suitable ARIMA models, and we have chosen the best model fit among them using AIC and BIC criteria. Findings: We have found that Serbia and North Macedonia show a mild growth in future investments. A significant percentage of the cumulative FDI inflows from EU companies have been invested precisely in Serbia, while in North Macedonia, fostering FDI has been promoted as one of the main instruments for employment and economic development. Oher Yugoslav countries tend to stagnate in the future period, which is in literature called a negative ‘Western Balkans’ effect on FDI. Contribution: Findings of the mild growth in FDI inflows in Serbia and North Macedonia contribute to the policy of attracting the FDI inflows in the countries of Southeastern Europe.

scholarly journals A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yanpeng Zhang ◽  
Hua Qu ◽  
Weipeng Wang ◽  
Jihong Zhao
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Time Series Clustering ◽  
Proposed Model ◽  
High Order Time

Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.

scholarly journals High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wangren Qiu ◽  
Xiaodong Liu ◽  
Hailin Li
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Composite Index ◽  
High Order ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Logical Relationship ◽  
Proposed Model ◽  
The University ◽  
Fuzzy Logical

In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted asGTS(M,N)on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the proposed model is implemented in forecasting enrollments of the University of Alabama. As an example of in-depth research, the proposed approach is also applied to forecast the close price of Shanghai Stock Exchange Composite Index. Finally, the effects of the number of orders and hierarchies of fuzzy logical relationships on the forecasting results are discussed.

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