A Time Series Analysis and Forecasting of Opening Stock Price of McDonalds Crop.

McDonalds Corp. is globally famous and is abounded in recent years. It is one of the major chain restaurants that offers a fast food. Basic foods that are served at McDonalds are different types and sizes of burgers, fries, some breakfast, sweets, ice cream and kids meals. McDonalds products have increased loyalty from customers, which has led to the rise of an uneven stock price. So the data is not stationary and makes the role of the analysts ability to forecast the future condition of the organization important. The aim of this paper is to analyze and forecast the opening stock price of McDonalds Corp. over a period time

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Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

Investment Management and Financial Innovations ◽

10.21511/imfi.17(4).2020.20 ◽

2020 ◽

Vol 17 (4) ◽

pp. 215-227

Author(s):

Julia Babirath ◽

Karel Malec ◽

Rainer Schmitl ◽

Kamil Maitah ◽

Mansoor Maitah

Keyword(s):

Time Series ◽

Risk Management ◽

Time Series Analysis ◽

Wiener Process ◽

Stock Price ◽

Percentage Error ◽

Portfolio Performance ◽

Stock Price Prediction ◽

Series Analysis ◽

Augmented Dickey Fuller

The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

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Seasonality in extra-pulmonary tuberculosis notifications in Germany 2004-2014- a time series analysis

BMC Public Health ◽

10.1186/s12889-021-10655-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Tanja Charles ◽

Matthias Eckardt ◽

Basel Karo ◽

Walter Haas ◽

Stefan Kröger

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Disease Onset ◽

Age Groups ◽

Age Group ◽

Pulmonary Tb ◽

Series Analysis ◽

Disease Site ◽

Extra Pulmonary

Abstract Background Seasonality in tuberculosis (TB) has been found in different parts of the world, showing a peak in spring/summer and a trough in autumn/winter. The evidence is less clear which factors drive seasonality. It was our aim to identify and evaluate seasonality in the notifications of TB in Germany, additionally investigating the possible variance of seasonality by disease site, sex and age group. Methods We conducted an integer-valued time series analysis using national surveillance data. We analysed the reported monthly numbers of started treatments between 2004 and 2014 for all notified TB cases and stratified by disease site, sex and age group. Results We detected seasonality in the extra-pulmonary TB cases (N = 11,219), with peaks in late spring/summer and troughs in fall/winter. For all TB notifications together (N = 51,090) and for pulmonary TB only (N = 39,714) we did not find a distinct seasonality. Additional stratified analyses did not reveal any clear differences between age groups, the sexes, or between active and passive case finding. Conclusion We found seasonality in extra-pulmonary TB only, indicating that seasonality of disease onset might be specific to the disease site. This could point towards differences in disease progression between the different clinical disease manifestations. Sex appears not to be an important driver of seasonality, whereas the role of age remains unclear as this could not be sufficiently investigated.

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Analyses and anticipating the future trend of accidents in an electricity distribution company of Iran: A time series analysis

Work ◽

10.3233/wor-213456 ◽

2021 ◽

pp. 1-6

Author(s):

Shirin Nasrollah Nejhad ◽

Tayebeh Ilaghinezhad Bardsiri ◽

Maryam feiz arefi ◽

Amin babaei poya ◽

Ehsan mazloumi ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Occupational Injuries ◽

Model Management ◽

High Rate ◽

Electricity Distribution ◽

Temporary Workers ◽

Series Analysis ◽

The Future ◽

Distribution Company

BACKGROUND: Many work-related fatalities happen every year in electricity distribution companies. This study was conducted to model accidents using the time series analysis and survey descriptive factors of injuries in an electricity distribution company in Tehran, Iran. METHODS: Data related to 2010 to 2017 were collected from the database of the safety department. Time Series and trend analysis were used for data analyzing and anticipating the accidents up to 2022. RESULT: Most of the accidents occurred in summer. Workers’ negligence was the reason for 75%of deaths. Employment type and type of injuries had a significant relationship (p < 0.05). CONCLUSION: The anticipating model indicated occupational injuries are going to have an increase in the future. A high rate of accidents in summer maybe because of the warm weather or insufficient skills in temporary workers. Temporary workers have no chance to work in a year like permanent workers, therefore acquisition experiences may be less in them. Based on the estimating model, Management should pay attention to those sectors of the company where most of the risky activities take place. Also, training programs and using personal protective equipment can help to protect workers in hazardous conditions.

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