scholarly journals Does the Baltic Dry Index predict economic activity in South Africa? A review from 1985 to 2016

2018 ◽  
Vol 21 (1) ◽  
Author(s):  
Kurt Sartorius ◽  
Benn Sartorius ◽  
Dino Zuccollo
Keyword(s):  
South Africa ◽  
Economic Activity ◽  
Research Question ◽  
Share Price ◽  
Series Data ◽  
Published Data ◽  
Data Set ◽  
Wide Range ◽  
The Baltic ◽  
Baltic Dry Index

Background: The ability of the Baltic Dry Index to predict economic activity has been evaluated in a number of developed and developing countries. Aim: Firstly, the article determines the primary factors driving the dynamics of the Baltic Dry Index (BDI) and, secondly, whether the BDI can predict future share price reactions on the Johannesburg Stock Exchange All Share Index (JSE ALSI), South Africa. Setting: This article investigates the dynamics and predictive properties of the BDI in South Africa between 1985 and 2016. Methods: The article uses a review of a wide range of published data and two time-series data sets to adopt a mixed methods approach. An inductive contents analysis is used to answer the first research question and a combination of a unit root test, correlation analysis and a Granger causality model is employed to test the second research question. Results: The results show that the BDI price is primarily driven by four underlying constructs that include the supply and demand for dry bulk shipping, as well as risk, cost and logistics management factors. Secondly, the results indicate a break in the BDI data set in July 2008 that influences a fundamental change in its relationship with the JSE ALSI index. In the pre-break period (1985 to 2008), the BDI is positively correlated with the ALSI (0.837, α = 0.05) before sharply diverging in the second period from August 2008 to 2016. In the first period, the BDI showed an optimal lag period of 6 months as a predictor of the ALSI index, but this predictive ability ceases after July 2008. The article makes a two-part contribution. Firstly, it demonstrates that the BDI is a useful predictor of future economic activity in an African developing country. Secondly, the BDI can be incorporated in government and industry sector planning models as a variable to assess future gross domestic product trends. Conclusion: The study confirms that the BDI is only a reliable indicator of future economic activity when the supply of shipping capacity is well matched with the demand.

scholarly journals Are Bulk/Container Shipping Companies Improving Compared to the Baltic Dry Index?

2019 ◽  
Vol 15 (12) ◽  
pp. 1
Author(s):  
Samuel D. Barrows
Keyword(s):  
Economic Activity ◽  
Stock Price ◽  
Time Frame ◽  
Container Shipping ◽  
Shipping Industry ◽  
Freight Rates ◽  
Industry Competition ◽  
Before And After ◽  
The Baltic ◽  
Baltic Dry Index

This study evaluates the 2000-2017 time frame and assesses the performance of the bulk/container shipping industry before and after the Great Financial Crisis (GFC) in relation to the Baltic Dry Index (BDI) and two other benchmarks in a variety of combinations. This study evaluates two different period portfolios of shipping companies based on their stock price total return performance. Five cases are presented that demonstrate portfolio improvement when comparing performance after the GFC with performance before the GFC in relation to the BDI and the other benchmarks. Included are discussions on shipping industry competition, vessel utilization and freight rates plus the BDI as an economic activity predictor.

Peer-to-peer-travel: is Airbnb a friend or foe to hotels?

2019 ◽  
Vol 13 (4) ◽  
pp. 443-457 ◽  
Author(s):  
Oswald Mhlanga
Keyword(s):  
South Africa ◽  
Empirical Evidence ◽  
Time Series Data ◽  
Theoretical Perspective ◽  
Hotel Industry ◽  
Disruptive Innovation ◽  
Series Data ◽  
Bottom Line ◽  
Data Set ◽  
Content Type

Purpose The sharing economy has caught great attention from researchers and policymakers. However, due to the dearth of available data, not much empirical evidence has been provided. This paper aims to empirically assess the impacts of Airbnb on hotel performances in South Africa. Design/methodology/approach Using South Africa as a case study, the study measures the impacts of Airbnb on hotel performances on three key metrics, namely, room prices, occupancy and Revenue per available room (RevPAR). A difference-in-difference model is estimated using a population-based data set of 809 hotels from 2016 to 2018. Findings The results reveal that despite Airbnb significantly and negatively impacting on hotel occupancies it has a non-significant effect on hotel prices and RevPAR. Although from the theoretical perspective a disruptive innovation business model such as Airbnb can possibly have a negligible effect on hotel performances because it may attract a different group of customers and create a new market, the empirical findings of this study fail to support this theoretical hypothesis. Consequently, the findings diverge with newly developed knowledge in other markets and point to nuanced and contextual complementary effects. Research limitations/implications Although some interesting findings are revealed into his study, some caveats remain. For instance, the study relied on data from hotels not from Airbnb. If the data of Airbnb can become available, it would be interesting to further examine whether the aggregated RevPAR of Airbnb can compensate for the aggregated loss of hotel RevPAR. This type of analysis could provide a broader evaluation scope regarding the overall effect of Airbnb on hotel performances. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel performances. Practical implications While hotels have launched a campaign to portray Airbnb as being commercial operators looking to compete illegally with hotels for the same segment of customers, this study shows that the rhetoric has been exaggerated. Airbnb, and more broadly, vacation rentals do not represent a war with hotels. They represent an answer to a different need. Indeed, the study reveals that Airbnb’s offer is a mere supplement to the market contrary to media rhetoric that it is meant to substitute hotels. The study has several implications for practitioners. First, these results are important because they serve as evidence against news articles that claim Airbnb is driving hotels out of business. They also show that if current trends continue, employees in the hotel industry in South Africa do not need to be concerned about losing their jobs because of Airbnb’s emergence. It is also important information for investors who may be concerned that Airbnb is hurting the hotel industry’s bottom line. Second, as the share of Airbnb listings on the accommodation market varies dramatically between cities, it is likely that eventual regulations/restrictions should be introduced in the provincial levels, while most of the cities continue benefiting from the increasing number of Airbnb visitors. Originality/value To the best of the author’s knowledge, this study is the first in South Africa to provide empirical evidence that Airbnb is significantly changing consumption patterns in the hotel industry, as opposed to generating purely incremental economic activity.

Airbnb and hotels: friends, enemies or frenemies?

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oswald Mhlanga
Keyword(s):  
South Africa ◽  
Time Series Data ◽  
Sharing Economy ◽  
Disruptive Innovation ◽  
Series Data ◽  
Data Set ◽  
Content Type ◽  
Complementary Role ◽  
Triple Difference ◽  
The Impact

Purpose The purpose of this paper is to explore the intricate relationship between the flagship of the sharing economy, Airbnb and hotel revenue per available room (RevPAR) in South Africa. Design/methodology/approach To identify the impact of Airbnb on hotel RevPAR, the paper used a triple difference-in-differences framework that compares changes in cities in South Africa where Airbnb started operating relative to areas without Airbnb. A total of 569 hotels were analysed. Findings While the study finds no evidence of adverse impacts of Airbnb on hotel RevPAR, the findings show that the entry of Airbnb led to a decrease in RevPAR of budget hotels. However, its impact is more pronounced during periods of peak demand, consequently, disrupting the pricing power of hotels. Research limitations/implications The research was based on the impact of Airbnb on hotel RevPAR in hotels situated in specific cities in South Africa. Caution is therefore required when generalising the findings of this study to other hotels in other geographic areas. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel RevPAR. However, the findings underscore the notion that innovations are not intrinsically disruptive but only relative to another product. In so doing, the study adds to the limited body of work in the field on disruptive innovation and to the academic discourse on innovation in tourism more broadly. Practical implications First, the findings suggest the impact on hotels tends towards Airbnb generally playing a largely complementary role rather than a diversionary one. However, to increase RevPAR, hotels should systematically change their pricing models to account for flexible capacity by rethinking the wisdom of seasonal pricing and reduce prices during peak seasons to avoid inviting more competition from Airbnb. Originality/value To the best of the author’s knowledge, this paper is the first to explore the relationship between Airbnb and hotel markets using a triple difference methodology.

scholarly journals A comprehensive study on battery electric modeling approaches based on machine learning

2021 ◽  
Vol 4 (S3) ◽  
Author(s):  
Felix Heinrich ◽  
Patrick Klapper ◽  
Marco Pruckner
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Model Complexity ◽  
Series Data ◽  
Data Set ◽  
Regression Methods ◽  
Wide Range ◽  
Electric Modeling ◽  
Conventional Regression ◽  
Comprehensive Study

AbstractBattery electric modeling is a central aspect to improve the battery development process as well as to monitor battery system behavior. Besides conventional physical models, machine learning methods show great potential to learn this task using in-vehicle data. However, the performance of data-driven approaches differs significantly depending on their application and utilized data set. Hence, a comparison among these methods is required beforehand to select the optimal candidate for a given task.In this work, we address this problem and evaluate the strengths and weaknesses of a wide range of possible machine learning approaches for battery electric modeling. In a comprehensive study, various conventional regression methods and neural networks are analyzed. Each method is trained and optimized based on a large and qualitative data set of automotive driving profiles. In order to account for the influence of time-dependent battery processes, both low pass filters and sliding window approaches are investigated.As a result, neural networks are found to be superior compared to conventional regression methods in terms of accuracy and model complexity. In particular, Feedforward and Convolutional Neural Networks provide the smallest average error deviations of around 0.16%, which corresponds to an RMSE of 5.57mV on battery cell level. With automotive time series data as focus, neural networks additionally benefit from their ability to learn continuously. This key capability keeps the battery models updated at low computational costs and accounts for changing electrical behavior as the battery ages during operation.

scholarly journals Experimental field data for modeling the growth response of tef to nitrogen fertilizer and water stress

2020 ◽  
Vol 5 ◽  
pp. 16-21
Author(s):  
Kirsten E Paff ◽  
Senthold Asseng ◽  
A. Araya ◽  
J. Davison ◽  
A. Getu ◽  
...  
Keyword(s):  
Nitrogen Fertilizer ◽  
Field Data ◽  
Time Series Data ◽  
Weather Data ◽  
Series Data ◽  
Data Set ◽  
Tigray Region ◽  
Fertilizer Experiment ◽  
Daily Weather Data ◽  
Wide Range

Field data from six experiments covering a wide range of growing conditions were organized for tef growth and cropping systems modeling. The data included (i) an irrigation experiment in the Tigray region of Ethiopia, (ii) a cultivar trial at Fallon, NV, USA, (iii) a nitrogen fertilizer experiment in the Jamma District of Ethiopia, (iv) a nitrogen fertilizer experiment in the Ofla District of Ethiopia, (v) a nitrogen fertilizer experiment in the Ada area of Ethiopia, and (vi) a nitrogen fertilizer experiment at Gare Arera, Ethiopia. The combined data set covered 40 experimental treatments and 131 observations. Time series data were limited to biomass data from two treatments from the Tigray region experiment. All other crop related data was measured at maturity. Daily weather data was taken primarily from satellite weather databases for Ethiopia, and from weather stations in the USA. These data have been used in various agronomic studies, as well as the calibration of the DSSAT Tef model. The results of this model calibration are also included in this paper. The objective of this paper was to present and preserve all of the field data used for calibrating the DSSAT Tef model, as well as the tef model’s simulations of the field data.

scholarly journals Anomaly detection in multidimensional time series— A graph-based approach

2021 ◽  
Author(s):  
Marcus Erz ◽  
Jeremy Floyd Kielman ◽  
Bahar Selvi Uzun ◽  
Gabriele Stefanie Guehring
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Distance Measures ◽  
Series Data ◽  
Data Set ◽  
Research Areas ◽  
Multidimensional Time Series ◽  
Wide Range ◽  
Time Frames

Abstract As the digital transformation is taking place, more and more data is being generated and collected.To generate meaningful information and knowledge researchers use various data mining techniques. In addition to classification, clustering, and forecasting, outlier or anomaly detection is one of the most important research areas in time series analysis. In this paper we present a method for detecting anomalies in multidimensional time series using a graph-based algorithm. We transform time series data to graphs prior to calculating the outlier since it offers a wide range of graph-based methods for anomaly detection. Furthermore the dynamics of the data is taken into consideration by implementing a window of a certain size that leads to multiple graphs in different time frames. We use feature extraction and aggregation to finally compare distance measures of two time-dependent graphs. The effectiveness of our algorithm is demonstrated on the Numenta Anomaly Benchmark with various anomaly types as well as the KPI-Anomaly-Detection data set of 2018 AIOps competition.

scholarly journals The Periodicity of Violent and Property Crime in Tshwane, South Africa

2016 ◽  
Vol 27 (1) ◽  
pp. 60-71 ◽  
Author(s):  
Ellen G. Cohn ◽  
Gregory D. Breetzke
Keyword(s):  
South Africa ◽  
Fourier Analysis ◽  
Time Series Data ◽  
Property Crime ◽  
Series Data ◽  
Mathematical Tool ◽  
Physical Sciences ◽  
Temporal Dimension ◽  
Data Set ◽  
Property Crimes

In this article, we identify and analyze the periodicity of violent and property crimes committed in Tshwane, South Africa, from 2001 to 2006. This is done using Fourier analysis, an advanced explorative mathematical technique commonly used in the physical sciences to detect the presence of a frequency or periodicity in a large time-series data set. The use of this technique in criminology is in its infancy, and in this study, Fourier analysis is used to identify periodic moments in time at which the risk of being a victim of violent and property crime in the city of Tshwane is heightened. Results indicated that violent crime peaks roughly every 7 and 75 days over the 5-year study period, with a marginal peak every 150 days. Property crimes peak every 75 days and every 150 days. Periodic peaks of crime observed in this study are explained using the central tenets of routine activities theory. Fourier analysis is an underused, powerful data-driven mathematical tool that should be added to the methodological arsenal available to criminologists when analyzing the temporal dimension of crime.

scholarly journals Strong Correlation Between Prevalence of Severe Vitamin D Deficiency and Population Mortality Rate from COVID-19 in Europe

2020 ◽  
Author(s):  
Isaac Z. Pugach ◽  
Sofya Pugach
Keyword(s):  
Vitamin D ◽  
Vitamin D Deficiency ◽  
Strong Correlation ◽  
Death Rate ◽  
Pearson Correlation ◽  
Published Data ◽  
Data Set ◽  
Severe Vitamin ◽  
Vitamin D Levels ◽  
Wide Range

AbstractBackgroundSARS-CoV-2 virus causes a very wide range of COVID-19 disease severity in humans: from completely asymptomatic to fatal, and the reasons behind it are often not understood. There is some data that Vitamin D may have protective effect, so authors decided to analyze European country-wide data to determine if Vitamin D levels are associated with COVID-19 population death rate.MethodsTo retrieve the Vitamin D levels data, authors analyzed the Vitamin D European population data compiled by 2019 ECTS Statement on Vitamin D Status published in the European Journal of Endocrinology. For the data set to used for analysis, only recently published data, that included general adult population of both genders ages 40-65 or wider, and must have included the prevalence of Vitamin D deficiency.ResultsThere were 10 countries data sets that fit the criteria and were analyzed. Severe Vitamin D deficiency was defined as 25(OH)D less than 25 nmol/L (10 ng/dL). Pearson correlation analysis between death rate per million from COVID-19 and prevalence of severe Vitamin D deficiency shows a strong correlation with r = 0.76, p = 0.01, indicating significant correlation. Correlation remained significant, even after adjusting for age structure of the population. Additionally, over time, correlation strengthened, and r coefficient asymptoticaly increased.ConclusionsAuthors recommend universal screening for Vitamin D deficiency, and further investigation of Vitamin D supplementation in randomized control studies, which may lead to possible treatment or prevention of COVID-19.

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