Analysis of Sales Pattern Predictions on New Segment Norms Using Linear Regression | Chapter 7 | Research and Applications Towards Mathematics and Computer Science Vol. 8

This work aims to create a system that identifies the different factors and predicts the most accurate sales for a particular region with the help of history in that particular history. This is to resolve all the challenges and problems that occur while making orders daily. Building this tool will increase the overall sales of the country by aiming at the potential areas of growth which will directly lead to an increase in sales. The Revenue Growth Management analysis generates a sales prediction basedonvarious factors which will help in targeting manufacturing and so on. The aim is to generate a system that predicts accurate sales so least of the products are being manufactured with no use. This will help in targeting the niche market of buyers who are potential buyers.

Author(s) Details:

Logeswari Saranya R.,
Department of AI&DS, RMK Engineering College, Thiruvallur-601206, India.

Please see the link here: https://stm.bookpi.org/RATMCS-V8/article/view/13120

Forecasting Daily Exchange Rates with Artificial Neural Network (ANN) | Chapter 3 | Current Topics on Business, Economics and Finance Vol. 2

 The forming and forecasting of supposed exchange rate dynamics has long existed a focus of financial and financial studies. Artificial intelligence (AI) modeling has currently received greatly of attention as a new method in business-related and financial predicting. This research suggests an alternate blueprint for forecasting regularly exchange rates that is based on affected neural network (ANN).Our practical research is based on a set of Tunisian everyday data. In order to judge this strategy, we compare allure performance to that of a statement autoregressive conditional heteroskedasticity (GARCH) model. The results show that the projected nonlinear autoregressive (NAR) model is a reliable and fast prediction means. This discovery admits businesses and policymakers to plan in a more excellent manner.

Author(s) Details:

Fahima Charef,
Department of Finance, FSEGT, University of Tunis Elmanar, Tunis, Tunisia.

Fethi Ayachi,
Department of economic, High School of Economics and Trade of Tunis, CEMAFI, Nice, France.

Please see the link here: https://stm.bookpi.org/CTBEF-V2/article/view/9790

Application of Solar Power in Telecom Tower to Reduce Environmental Pollution | Chapter 7 | Current Advances in Geography, Environment and Earth Sciences Vol. 6

 The significant issue brought on by the rise in energy consumption and the environmental "emergency" brought on by the emission of greenhouse gases over the past ten years have compelled people to develop new strategies and technologies for the production of primary energy that can satisfy the escalating and rising energy demand. One of the most important global challenges of our day is climate change. The typical temperature has greatly increased. The effect of global warming, which has caused an increase in greenhouse gases (GHG) in the atmosphere, is responsible for this temperature rise. The rise in energy use, which leads to higher emissions of pollutants, is the cause of the increase in GHG, primarily carbon dioxide (CO2). As a result, growing telecommunication infrastructure requires more electricity to operate, which is why we think our topic title is appropriate. This essay examines how an efficient energy management system, combined with energy and environmental auditing, pave the way for green telecom in terms of green telecom networks, maintenance of green telecom equipment, design of green telecom buildings, and disposal of green telecom trash. The presentation centred on how solar energy is employed in telecom towers, as well as research done using case studies and governmental directives to meet the requirement of strategic fit compliances for environmental protection.

Author(s) Details:

Madhu Kumari,
NIFFT, Hatia, Ranchi, India.

Please see the link here: https://stm.bookpi.org/CAGEES-V6/article/view/8265

A Case Study of Time Series Analysis to Forecast the Number of Patients with Tuberculosis in Khartoum State from 2007 to 2016 | Chapter 12 | New Innovations in Economics, Business and Management Vol. 5

 The number of tuberculosis (TB) cases in Khartoum state was predicted using time series analysis. It is based on data collected from tuberculosis patients between 2007 and 2016. The investigation was successful in determining the optimal data model of order (2) ARIMA (2, 1, 0). The study's most noteworthy finding is a quartile-based projection of the number of people with tuberculosis in the next four years. As a result, the predicted value was found to drop and represented the source time series data. The study's ineffectiveness derives from the utilisation of TB data and, as a result, the estimation of future patient numbers, which benefits both patients and specialists.

Author(S) Details

Abu Elgasim Abbas Abow Mohammed
College of Business and Economics, Qassim University, Kingdom of Saudi Arabia and College of Economic and Political Science Omdurman Islamic University, Sudan.

View Book:- https://stm.bookpi.org/NIEBM-V5/article/view/5624

A Framework for Developing Agricultural Product Forecasting System Application for Farmers | Chapter 12 | Advanced Aspects of Engineering Research Vol. 16

 Agriculture employs one-third of the workers in the Philippines. Farmers were critical in ensuring that fresh food was delivered to every home on a regular basis, which was necessary for our health. The agriculture sector's long-term viability was improved by assisting farmers in developing a stronger network to market their products. By ensuring that produced products are profitable, the creation of an agricultural forecasting mobile application will help both farmers and dealers. The developed architecture design will be used in agricultural forecasting applications. The research looked into the factors that went into creating a smartphone application to assist farmers.

The use of a variety of statistical methodologies facilitated in the provision of in-depth analysis, resulting in a design that was more universally accepted by farmers.

The findings show that addressing certain common concerns about providing online services might help solve some problems and answer questions about agricultural output forecasting best practises.

According to the report, having a mobile agricultural forecasting tool alleviated the issues with farm waste generation.

Farmers will be able to use the app to determine the level of crop demand in the market and navigate to the location in the participating trading post where farm produce demand is high. Farmers and the government were able to improve the agricultural sector, which was trailing behind that of other developing countries.

Author (S) Details

Joe Marie D. Dormido
College of Computer Studies, Carlos Hilado Memorial State College, Bacolod City, Philippines.

Thelma D. Palaoag
College of Information Technology and Computer Science, University of the Cordilleras, Baguio City, Philippines.

View Book :- https://stm.bookpi.org/AAER-V16/article/view/1997

Study on Time Series Modeling and Forecasting Inflation: Evidence from Nigeria | Chapter 10 | Insights into Economics and Management Vol. 4

Effective prediction of general price level trends has been a major concern of entrepreneurs and monetary authorities in Nigeria over the past decades. The results allow monetary authorities to prepare effectively and the profit drive on the part of entrepreneurs and investors continues. This research uses a univariate model developed by Box and Jenkins in the form of the Autoregressive Integrated Moving Average model and a multivariate time series model to forecast inflation for Nigeria in the form of the Vector Autoregressive model. Changes in the monthly use of this paper For the period 2003 to 2012, the consumer price index collected from the National Bureau of Statistics and the Central Bank of Nigeria forecast movements in the general price level. The best forecasting model for predicting inflation in Nigeria is defined on the basis of various diagnostic and evaluation parameters. The results would enable policymakers and businesses to monitor the output and stability of key macroeconomic indicators using the inflation forecast. For the month of 2012:10, the study expected core inflation using VAR to be 11.06 percent. A major drawback of this study is that the VAR method and the ARIMA method based on two major forecasting methods and ignored the use of neural network analysis. Furthermore, as an indicator of inflation, only core inflation was used. Subsequent inflation forecasting studies in Nigeria should aim to predict inflation through a broader variety of inflation steps.

Author (s) Details

Dr. Ikechukwu Kelikume
Lagos Business School, K.M 22 Lekki-Aja Expressway, Lagos Island, Lagos State, Nigeria.

Adedoyin Salami
Lagos Business School, K.M 22 Lekki-Aja Expressway, Lagos Island, Lagos State, Nigeria.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/348