scholarly journals Economics of apple-storage I: Comparative time series analysis of apple producer prices in Germany and Hungary

2010 ◽  
Vol 16 (4) ◽  
Author(s):  
Z. Lakner ◽  
F. Apáti
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Stepwise Regression ◽  
Storage Capacity ◽  
Arima Models ◽  
Domestic Markets ◽  
Series Analysis ◽  
Econometric Methods ◽  
Producer Prices ◽  
High Storage

Based on standard econometric methods the article analyses the time series of fresh apple producer prices in domestic markets of Germany and Hungary. In Germany, as a consequence of high storage capacities the quantity offered in different parts of the season is relatively stable, that’s why only a rather limited price increasing can be detected. In Hungary, as a consequence of the limited storage capacity this fluctuation is much more important. The modern methods of time series analysis (ARIMA models, stepwise regression) can be efficiently applied for forecasting of price movements.

scholarly journals Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 480
Author(s):  
Rania Kousovista ◽  
Christos Athanasiou ◽  
Konstantinos Liaskonis ◽  
Olga Ivopoulou ◽  
George Ismailos ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Acinetobacter Baumannii ◽  
Regression Models ◽  
Tertiary Care ◽  
Antimicrobial Use ◽  
Care Hospital ◽  
Arima Models ◽  
Series Analysis ◽  
Resistance Rates

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R2 > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

scholarly journals Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence

2016 ◽  
Vol 15 (1) ◽  
Author(s):  
Mohammad Y. Anwar ◽  
Joseph A. Lewnard ◽  
Sunil Parikh ◽  
Virginia E. Pitzer
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Future Trends ◽  
Arima Models ◽  
Series Analysis

scholarly journals STUDY OF THE INFLUENCE OF CHANGING THE PARAMETERS OF THE ARIMA MODEL ON THE QUALITY OF THE FORECAST FOR SHORT DATA SETS

2021 ◽  
Vol 21 (3) ◽  
pp. 36-46
Author(s):  
M.N. Fel’ker ◽  
◽  
V.V. Chesnov
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Arima Model ◽  
Annual Data ◽  
Data Sets ◽  
Arima Models ◽  
Data Set ◽  
Series Analysis ◽  
The Future

Time series, i.e. data collected at various times. The data collection segments may differ de-pending on the task. Time series are used for decision making. Time series analysis allows you to get some result that will determine the format of the decision. Time series analysis was carried out in very ancient times, for example, various calendars became a consequence of the analysis. Later, time series analysis was applied to study and forecast economic, social and other systems. Time se-ries appeared a long time ago. Once upon a time, ancient Babylonian astronomers, studying the po-sition of the stars, discovered the frequency of eclipses, which allowed them to predict their appearance in the future. Later, the analysis of time series, in a similar way, led to the creation of various calen-dars, for example, harvest calendars. In the future, in addition to natural areas, social and economic ones were added. Aim. Search for classification patterns of time series, allowing to understand whether it is possible to apply the ARIMA model for their short-term (3 counts) forecast. Materials and methods. Special software with ARIMA implementation and all need services is made. We examined 59 data sets with a short length and step equal a year, less than 20 values in the paper. The data was processed using Python libraries: Statsmodels and Pandas. The Dickey – Fuller test was used to de-termine the stationarity of the series. The stationarity of the time series allows for better forecasting. The Akaike information criterion was used to select the best model. Recommendations for a rea-sonable selection of parameters for adjusting ARIMA models are obtained. The dependence of the settings on the category of annual data set is shown. Conclusion. After processing the data, four categories (patterns) of year data sets were identified. Depending on the category ranges of parame-ters were selected for tuning ARIMA models. The suggested ranges will allow to determine the starting parameters for exploring similar datasets. Recommendations for improving the quality of post-forecast and forecast using the ARIMA model by adjusting the settings are given.

scholarly journals Time series analysis of malaria in Kumasi: Using ARIMA models to forecast future incidence

2018 ◽  
Vol 4 (1) ◽  
pp. 1461544 ◽  
Author(s):  
Reindolf Anokye ◽  
Enoch Acheampong ◽  
Isaac Owusu ◽  
Edmund Isaac Obeng ◽  
Yan Lin
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Arima Models ◽  
Series Analysis

scholarly journals Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Bin Xu ◽  
Jiayuan Li ◽  
Mengqiao Wang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Goodness Of Fit ◽  
Intervention Strategy ◽  
Age Groups ◽  
Arima Models ◽  
Optimal Model ◽  
Series Analysis ◽  
Infectious Disease Reporting ◽  
Significant Difference

Abstract Background To investigate the regional and age-specific distribution of AIDS/HIV in China from 2004 to 2017 and to conduct time series analysis of the epidemiological trends. Method Using official surveillance data from publicly accessible database of the national infectious disease reporting system, we described long-term patterns of incidence and death in AIDS/HIV, analyzed age group and regional epidemic characteristics, and established Autoregressive Integrated Moving Average (ARIMA) models for time series analysis. Result The incidence and death of AIDS/HIV have increased rapidly from 2004 to 2017, with significant difference regarding age groups and provincial regions (a few provinces appear as hot spots). With goodness-of-fit criteria and using data from 2004 to 2015, ARIMA (0,1,3) × (2,0,0), ARIMA (3,1,0) × (1,0,1), and ARIMA (0,1,2) × (2,0,0) were chosen as the optimal model for the incidence of AIDS, HIV, and combined; ARIMA (0,1,3) × (1,0,0) was chosen as the optimal model for the death of AIDS, HIV, and combined. ARIMA models robustly predicted the incidence and death of AIDS/HIV in 2016 and 2017. Conclusion A focused intervention strategy targeting specific regions and age groups is essential for the prevention and control of AIDS/HIV. ARIMA models function as data-driven and evidence-based methods to forecast the trends of infectious diseases and formulate public health policies.

scholarly journals Price Parities for Vegetables in Serbia - Analysis and Forecasting

2019 ◽  
Vol 68 (3-4) ◽  
pp. 51-59
Author(s):  
Nebojša Novković ◽  
Ljiljana Drinić ◽  
Šumadinka Mihajlović ◽  
Nataša Vukelić ◽  
Dragan Ivanišević
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Descriptive Statistics ◽  
Agricultural Products ◽  
Price Formation ◽  
Vegetable Crops ◽  
Arima Models ◽  
Research Results ◽  
Series Analysis ◽  
Point Of Reference

Summary The paper analyzes price parities of important vegetable crops in Serbia in relation to wheat, which has always been a point of reference in price formation of other agricultural products. The analysis was carried out by means of descriptive statistics for the period 1994-2017 for the following vegetable crops: potato, bean, tomato, pepper, onion and cabbage. The method used for forecasting of the price parities for the period 2018-2022 is time series analysis, i.e. ARIMA models. The research results showed that the price parities of bean, tomato and pepper will increase: from 9.1 to 12.3 for bean, from 1.9 to 3.5 for tomato and from 2.3 to 3 for pepper. The price parities for potato (1.4) and cabbage (1.4) will remain practically unchanged, while the price parity of onion will decrease to 1.5.

scholarly journals Pengaruh Komponen Neraca Air Terhadap Produktivitas Kelapa Sawit pada Berbagai Jenis Tanah:: Studi Kasus Kalimantan Tengah dan Barat

2021 ◽  
Vol 29 (1) ◽  
pp. 11-20
Author(s):  
Adhy Ardiyanto ◽  
Kukuh Murtilaksono ◽  
Enni Dwi Wahjunie ◽  
Atang Sutandi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Stepwise Regression ◽  
Series Analysis ◽  
Area 5 ◽  
Area 6

Perbedaan jenis tanah dan letak geografis kebun kelapa sawit menyebabkan variasi sifat fisik tanah (terutama tekstur dan porositasnya).  Perbedaan letak geografis juga menyebabkan variasi jumlah curah hujan yang mempengaruhi neraca air lahan. Respons fluktuasi produksi kelapa sawit yang berbeda akibat variasi jenis tanah dan neraca air lahan dapat dikaji dengan analisis hubungan neraca air lahan dan produktivitas kelapa sawit antar jenis tanah dan antar wilayah. Pada penelitian ini, analisis neraca air lahan dilakukan pada tiga lokasi area perkebunan kelapa sawit PT Bumitama Gunajaya Agro yang berada di provinsi Kalimantan Tengah dan Kalimantan Barat, menggunakan metode Thornthwaite-Mather. Analisis hubungan komponen neraca air dengan produksi TBS kelapa sawit dihitung berdasarkan data bulanan selama 8 tahun (2012 – 2019) menggunakan time series analysis dan untuk mencari model regresi terbaik digunakan stepwise regression.  Komponen neraca air yang paling mempengaruhi produksi tandan buah segar (TBS) kelapa sawit (ton/ha/bulan) adalah curah hujan 7 bulan sebelum panen, hari hujan 11 dan 28 bulan sebelum panen, dan defisit air 10 bulan sebelum panen.  Penurunan produksi TBS (ton/ha/bulan) terjadi setiap ada kenaikan defisit air senilai 10 mm/bulan pada tanah Dystrudept, Paleudults dan Haplohumods di Area 3 berturut-turut adalah 11, 2, dan 3%. Pada Area 5 penurunannya adalah 12, 4, dan 5%. Pada Area 6 penurunannya adalah 5, 18 dan 3%.

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