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Forecasting Wheat Production in India Using ARIMA and Radial Basis Function
Subbiah Selvakumar,
Veluchamy Kasthuri
Issue:
Volume 8, Issue 4, December 2022
Pages:
61-66
Received:
9 September 2022
Accepted:
14 October 2022
Published:
29 November 2022
DOI:
10.11648/j.ijdst.20220804.11
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Abstract: The time series is an arrangement of values in a specific order of time. Time series analysis, mostly used for forecasting. Prediction and analysis of wheat is a vital role in agricultural statistics. Indian wheat is largely a soft/medium hard, medium protein, white bread wheat, somewhat similar to U.S. hard white wheat. India is the second largest producer of wheat. The Agriculture Statistics System is very complete and provides data on a wide range of topics such as crop area and production, land use, water irrigation, land holdings, etc. Agricultural credit and subsidies are also considered important supporting factors for agriculture growth. Food grain production covers the dominant part of the cropped area (65%) in Indian agriculture. India is the world's largest producer of millets and second-largest producer of wheat, rice, and pulses. The present research work focused on the production of wheat in India using time series data ranging from 2001 to 2021. In this paper, Autoregressive Integrated Moving Average Model (ARIMA) and Radial Basis Function (RBF) for predicting wheat production of India was compared. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were compared. The outcomes were displayed numerically and graphically.
Abstract: The time series is an arrangement of values in a specific order of time. Time series analysis, mostly used for forecasting. Prediction and analysis of wheat is a vital role in agricultural statistics. Indian wheat is largely a soft/medium hard, medium protein, white bread wheat, somewhat similar to U.S. hard white wheat. India is the second largest...
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Potentials of Blockchain Technology in Streamlining Land Registries
Issue:
Volume 8, Issue 4, December 2022
Pages:
67-71
Received:
14 November 2022
Accepted:
2 December 2022
Published:
15 December 2022
DOI:
10.11648/j.ijdst.20220804.12
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Views:
Abstract: Disruptive technologies are currently taking the world at a storm. In all aspects of life, people are trying to cope up with the effects of these technologies while in other circumstances, focusing on research and development of enhanced ways to efficiency at work and daily activities. Land registry system is a very important element in the society, especially in regards to social and economic developments of an area. With the current traditional land registry system used in most countries, there are many issues that need to be sorted for increased efficiency. The issues may range from corruption and fraud, time-consuming registration and transfer of properties, inefficient systems in place, backlogs in deeds and contracts, and tampering of records amongst others. The objective of this paper is to analyze the issues present in the conventional land registry and explore the potentials of streamlining them using blockchain concepts. With the concepts of blockchain technology, a secure and more reliable framework is promised in the land registry system. Literature review and analysis has been extensively used to bring out the concepts in this paper. The issues in the traditional land registry are also analyzed, including the different classification of land registry systems among different governments. The blockchain concepts and the potentials of implementations are finally explained succinctly, with their advantages and disadvantages. The author concludes that there are massive advantages when compared to the disadvantages in the adoption of blockchain technology in the land registries.
Abstract: Disruptive technologies are currently taking the world at a storm. In all aspects of life, people are trying to cope up with the effects of these technologies while in other circumstances, focusing on research and development of enhanced ways to efficiency at work and daily activities. Land registry system is a very important element in the society...
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Forecasting of Global Stock Market by Two Stage Optimization Model
Issue:
Volume 8, Issue 4, December 2022
Pages:
72-86
Received:
15 November 2022
Accepted:
6 December 2022
Published:
15 December 2022
DOI:
10.11648/j.ijdst.20220804.13
Downloads:
Views:
Abstract: Currently, about half of the transactions in the US stock market are based on high-frequency algorithmic trading, making it difficult for the investors with the long-term investment horizon, such as pension funds, to obtain stable returns. The development of a market forecast model that could achieve stable returns over the long term is an important issue in supporting not only pensions but also the central bank policy makers or new private businesses. To obtain stable investment performance by a forecast model over the long-term, it is necessary to remove noise from sample data in advance and extract a universal pattern. However, it is difficult to preliminarily distinguish between noise and true patterns and remove noise in advance. In this study, the sample space was divided into 8 sub-spaces using a Two Stage Optimization decision tree, and the versatility of each sub-space was evaluated by a pattern recognition model. Then, the sub-space with a low versatility was defined as the space with relatively large noise, and a forecast model was created by excluding the sub-spaces with large noise. It was found that the forecast model constructed in this way could obtain the prediction accuracy higher than that of the conventional method. Also, when the prediction accuracy of the model was evaluated by the walk-forward method using financial time-series data, investment performance that stably exceeded the return of benchmark assets was obtained over the past 15 years.
Abstract: Currently, about half of the transactions in the US stock market are based on high-frequency algorithmic trading, making it difficult for the investors with the long-term investment horizon, such as pension funds, to obtain stable returns. The development of a market forecast model that could achieve stable returns over the long term is an importan...
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