A Time Series Forecasting Method

The fluctuating price of rubber is often detrimental to farmers because farmers generally cannot manage the timing of the sale to get a profitable selling price. High price fluctuations provide opportunities for traders to manipulate price information on farmers and cause farmers not to enjoy the higher true price. This research aims to see how risky the price is received by rubber farmers and its effect on farm income in West Aceh District. This study uses the ARCH-GARCH method and VaR calculations for price risk analysis and Simple Linear Analysis to analyze the effect of price on farm income. The results showed that the price risk received by rubber farmers was high, namely 41.699% during the one-year sales period. The regression results show that the price significantly affects the income of rubber farming in West Aceh Regency with a probability of 0.000. The value of R2 is 0.935, which means that rubber prices influence 93% of rubber farming income, and variables outside the model influence 7%. The selling price of rubber influences the income of rubber farming. If the price of rubber is low, the farmers cannot afford to pay for rubber maintenance, thus disrupting rubber productivity. Decreased productivity will reduce farm income in the West Aceh Regency, which means that the price of rubber influences 93% of rubber farming income, and 7% is influenced by variables outside the model.

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“…The data used in this research is secondary data in the form of data time series (time series) years [14]. Research time span 2008-2019.…”

Section: B Methods Of Collecting Datamentioning

confidence: 99%

Analysis of the Risk of Rubber Prices Using the ARCH-GARCH and VaR Methods on Farm Income in West Aceh District

Usman¹,

Wardhana²,

Istiqamah³

2022

International Journal on Advanced Science, Engineering and Information Technology

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“…Then, numerous algorithms have been existing and developed in the predictions area, from traditional until intelligent algorithms. In this paper, historical data on crude oil palm production have been implemented to be analysed using intelligent algorithms [11][12] [13]. This section will briefly explain a predictions, the BPNN algorithm and historical data used.…”

Section: Methodsmentioning

confidence: 99%

Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

Aini¹,

Haviluddin

2019

Kno. Eng. Da. Sc.

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Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in 2019.

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“…Time series methods are generally better suited for short to medium-term forecasting horizons (Wang et al, 2017).…”

Section: Limitations Of Tsmmentioning

confidence: 99%

Strategic analysis of Vilnius airport’s growth trajectory and noise regulation compliance

Zavadskas,

Grubliauskas,

Vaišis

2024

27-Osios Jaunųjų Mokslininkų Konferencijos „Mokslas – Lietuvos Ateitis“ Teminės Konferencijos DARNI APLINKA Straipsnių Rinkinys

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Accurate forecasting of future events is essential for managing and optimizing airport activities, facilitating the minimization of adverse environmental impacts. The availability of detailed information about yearly aircraft movements at Vilnius City International Airport (VNO) offers an opportunity to create various scenarios that projects the growth patterns within airport and aviation industry in general. This scientific research paper explores the temporal trajectory of VNO with a focus on forecasting its evolution towards reaching major airport status of 50 000 aircraft movements per year. The study employs quantitative method in forecasting data with cycling origin using the ratio to moving average method also known as Time Series Method (TSM). Calculations are done by Microsoft Excel software with which regression trend line is obtained. Two various scenarios are projected: the continuous use of Terminal 1 (T1) and the introduction of new Terminal 2 (T2). Forecast show that continuous use of T1 will reach threshold of 50 000 flights per year by end of 4th quarter of 2025. For T2 it is projected that 53 110 flights will be done at VNO at the end of the same year T2 is projected to become operational: 2026 4th quarter. These findings underscore the necessity of strategic planning and infrastructure development to accommodate future growth and increase airport efficiency in line with growing demands.

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A Time Series Forecasting Method

Cited by 4 publications

References 10 publications

Analysis of the Risk of Rubber Prices Using the ARCH-GARCH and VaR Methods on Farm Income in West Aceh District

Analysis of the Risk of Rubber Prices Using the ARCH-GARCH and VaR Methods on Farm Income in West Aceh District

Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

Strategic analysis of Vilnius airport’s growth trajectory and noise regulation compliance

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