scholarly journals Time Series Analysis and Forecasting Techniques for Foreign Direct Investment

2021 ◽  
Vol 6 (9) ◽  
pp. 391-397
Author(s):  
Ummi Rohaizad Abdul Rahim ◽  
Zahayu Md Yusof
Keyword(s):  
Time Series ◽  
Foreign Direct Investment ◽  
Time Series Analysis ◽  
Direct Investment ◽  
Equity Capital ◽  
Short Term ◽  
Series Analysis ◽  
Business Metrics ◽  
Forecasting Techniques

Foreign direct investment are the net inflows of investment to acquire a lasting management interest which is 10 percent or more of voting stock in an enterprise operating in an economy other than the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This paper will discuss the definitions and findings of previous studies regarding Foreign Direct Investment. This paper also will explain about forecasting techniques used in previous studies in forecasting Foreign Direct Investment. Time Series Analysis is used to determine a good model that can be used to forecast business metrics.

The Contribution of Foreign Direct Investment for Economic Growth of Ethiopia: Time Series Analysis

2018 ◽  
Vol 9 (02) ◽  
Author(s):  
Addissie Melak
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Foreign Direct Investment ◽  
Time Series Analysis ◽  
Direct Investment ◽  
Policy Framework ◽  
Annual Growth Rate ◽  
African Countries ◽  
Series Analysis ◽  
Integration Test

Economic growth of countries is one of the fundamental questions in economics. Most African countries are opening their economies for welcoming of foreign investors. As such Ethiopia, like many African countries took measures to attract and improve foreign direct investment. The purpose of this study is to examine the contribution of foreign direct investment (FDI) for economic growth of Ethiopia over the period of 1981-2013. The study shows an overview of Ethiopian economy and investment environment by the help of descriptive and econometric methods of analysis to establish empirical investigation for the contribution of FDI on Ethiopian economy. OLS method of time series analysis is employed to analyse the data. The stationary of the variables have been checked by using Augmented Dickey Fuller (ADF) Unit Root test and hence they are stationery at first difference. The co- integration test also shows that there is a long run relationship between the dependent and independent variables. Accordingly, the finding of the study shows that FDI, GDP per capita, exchange rate, total investment as percentage of GDP, inflow of FDI stock, trade as percentage of GDP, annual growth rate of GDP and liberalization of the economy have positive impact on Ethiopian GDP. Whereas Gross fixed domestic investment, inflows of FDI and Gross capital formation influence economic growth of Ethiopia negatively. This finding suggests that there should be better policy framework to attract and improve the volume of FDI through creating conducive environment for investment.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals A time series analysis of determinants of FDI in Afghanistan: Which one matters the most, security or economic growth?

2018 ◽  
Vol 6 (2) ◽  
pp. 19
Author(s):  
Abdul Fareed Delawari
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Foreign Direct Investment ◽  
Time Series Analysis ◽  
Direct Investment ◽  
Economic Factors ◽  
Market Economic ◽  
The Government ◽  
The Relationship

Afghanistan has been practicing market economic system since 2002. Since then, the government has been initiating different policies and announced various incentives to attract foreign direct investment (FDI) to the country. However, the outcome has not been satisfactory due to several political and economic factors. This paper explores the relationship between security, economic growth and FDI in Afghanistan, using ARDL model. The paper covers a period from 2002 to 2016. The empirical results of this study show that there is a negative long-term relationship between security and FDI. Hence,  the author concludes that, to attract FDI to the country, insuring security should be the top priority of the government of Afghanistan.

Relation between Land Subsidence and Deep Groundwater Exploitation in Cangzhou City

2013 ◽  
Vol 864-867 ◽  
pp. 2213-2217 ◽  
Author(s):  
Ju Yan Zhu ◽  
Hai Peng Guo
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Land Subsidence ◽  
Quantitative Relationship ◽  
Quantitative Relation ◽  
Short Term ◽  
Deep Groundwater ◽  
Groundwater Exploitation ◽  
Series Analysis

Due to long-term excessive exploitation of groundwater, serious land subsidence has been caused in Cangzhou City, Hebei Province, China. With GIS spatial analysis method, this paper conducted an analysis of the quantitative relationship between deep groundwater exploitation and the land subsidence in this area. This quantitative relation was analyzed by using data of both long-term and short-term time series. The long-term time series analysis indicates that the land subsidence volume accounts for 57.6% of the amount of deep groundwater exploitation, indirectly showing the proportion of released water from compressibility of the aquifers and the aquitards in deep groundwater exploitation. Some factors such as hysteresis effects of subsidence may be ignored in the short-term time series analysis, thus the calculated ratio becomes significantly large. From perspective of water resources evaluation, the long-term time series analysis is better to analyze the relation between land subsidence and deep groundwater exploitation.

Sign in / Sign up

Export Citation Format

Share Document