Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models
Cecilia Mbithe Titus,
Anthony Wanjoya,
Thomas Mageto
Issue:
Volume 7, Issue 1, February 2021
Pages:
1-7
Received:
26 January 2021
Accepted:
6 February 2021
Published:
10 February 2021
Abstract: Commercial farming of Guinea Fowls is at its infant stages and is generating a lot of interest for farmers in Kenya. This, coupled with an increased demand for poultry products in the Kenyan market in the recent past, calls for the rearing of the guinea fowls which are birds reared for meat and partly for eggs. In order to have an efficient production of poultry products for this type of poultry farming, there is need for an efficient modeling using sound statistical methodologies. It’s in this regard that the study modeled Guinea Fowl production in Kenya using the Univariate Auto-Regressive Integrated Moving Average (ARIMA) and the Auto-Regressive Fractional Integrated Moving Average (ARFIMA) models. Yearly guinea fowl production data for the period of 2010 to 2019 obtained from Food and Agricultural Organization (FAO-Kenya) was used in the study in which the Augmented Dickey Fuller (ADF) test was used to check for stationarity while the Hurst Exponent was used to test the long-memory property of the series. The ARIMA and ARFIMA models gave a better fit to the data and were used to forecast Guinea Fowl Weights. Fitted model forecast were evaluated via the Random Mean Squared Error (RMSE) in which the ARFIMA model was found to give a better forecast of the Guinea Fowl weights compared to the ARIMA model.
Abstract: Commercial farming of Guinea Fowls is at its infant stages and is generating a lot of interest for farmers in Kenya. This, coupled with an increased demand for poultry products in the Kenyan market in the recent past, calls for the rearing of the guinea fowls which are birds reared for meat and partly for eggs. In order to have an efficient product...
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Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya
Jackline Chepkorir Lang’at,
Thomas Mageto,
Irene Irungu
Issue:
Volume 7, Issue 1, February 2021
Pages:
8-12
Received:
3 February 2021
Accepted:
10 February 2021
Published:
27 February 2021
Abstract: Poverty and its alleviation schemes remain to be of much concern to many countries in the world. In the Sub-Saharan Africa, 41% of the population live below the extreme poverty line and in Kenya, almost 80% of the population are deemed poor. The Kenyan rural sector has a contribution of 40% to this poverty levels despite agriculture being the backbone and the main source of livelihood in the rural areas. It is in this regard that the study evaluates the household characteristics effect on Poverty indices among Crop Farmer Households. The Beta and Dirichlet regression models were used in the analysis in which the Beta regression model gave a better fit to the poverty indices data. The standardized residuals, probability plots, Chi-square test of association and the Breusch Pagan test for heteroscedasticity were used as goodness of fit evaluation tests in which levels of deprivation had a significant effect on the poverty indices among the crop farmers. Data used in the study was secondary data obtained from the Kenya National Bureau of Statistics Survey Consumption Index in Uasin Gishu County for the period March 2018 to May 2018 in which a total of 489 households were employed in the survey.
Abstract: Poverty and its alleviation schemes remain to be of much concern to many countries in the world. In the Sub-Saharan Africa, 41% of the population live below the extreme poverty line and in Kenya, almost 80% of the population are deemed poor. The Kenyan rural sector has a contribution of 40% to this poverty levels despite agriculture being the backb...
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Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market
Daniel Pyeong Kang Kim,
Jongwhee Lee,
Jungwoo Lee,
Jeanne Suh
Issue:
Volume 7, Issue 1, February 2021
Pages:
13-19
Received:
28 February 2021
Accepted:
16 March 2021
Published:
26 March 2021
Abstract: Over the past few years, Twitter has rapidly grown into a prominent social media platform, and various research papers have attempted to prove the relationship between the stocks and the tweets made on Twitter. The purpose of this research paper is to investigate the specific connection between Elon Musk’s twitter and the stock value of Tesla. The primary form of analysis used was Exploratory Data Analysis to be able to more easily distinguish patterns within our dataset, which was preprocessed to exclude any stopwords. Utilizing various graphs and Machine Learning algorithms such as Logistic Regression and Support Vector Machine, we wrote this research paper that respectively analyzes the change in the close price of Tesla’s stock and Elon Musk’s Twitter engagement, including tweets, likes, and retweets dating from the start of 2015 up until July of 2020. Furthermore, the article illustrates the contents of Elon Musk’s tweets and allows a deeper understanding of other correlations that may exist through the use of Machine Learning to perform Sentiment Analysis. This was achieved by categorizing Elon’s tweets into three different tones (positive, negative, and neutral) and seeing how the underlying mood would correspondingly affect Tesla’s stock value. The combination of such techniques and factors allowed for a conclusive result in which a distinct correlation was apparent: an increase in the number of tweets/engagement would lead to an increase in the closing price of Tesla, as well as vice versa.
Abstract: Over the past few years, Twitter has rapidly grown into a prominent social media platform, and various research papers have attempted to prove the relationship between the stocks and the tweets made on Twitter. The purpose of this research paper is to investigate the specific connection between Elon Musk’s twitter and the stock value of Tesla. The ...
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