Application of Exponential Smoothing Models and Arima Models in Time Series Analysis from Telco Area

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.

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Analysis of Surface Water Quality in Tehran

Water Quality Research Journal ◽

10.2166/wqrj.2002.032 ◽

2002 ◽

Vol 37 (2) ◽

pp. 489-511 ◽

Cited By ~ 3

Author(s):

Golamreza Asadollah-Fardi

Keyword(s):

Water Quality ◽

Time Series ◽

Surface Water ◽

Time Series Analysis ◽

Surface Water Quality ◽

Exponential Smoothing ◽

Future Design ◽

Series Analysis

Abstract Box-Jenkins and exponential smoothing time series analysis of the monthly water quality in surface water in Tehran was conducted as a case study. Various univariate models were developed for each determinand. Most of the models were seasonal, indicating that the water quality determinands vary throughout the year. The models follow the same pattern of variations present in the data. This study shows the credibility of the models, therefore the models may be used for future design purposes.

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

Malaria Journal ◽

10.1186/s12936-016-1602-1 ◽

2016 ◽

Vol 15 (1) ◽

Cited By ~ 33

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

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A New Approach of Brown’s Double Exponential Smoothing Method in Time Series Analysis

Balkan Journal of Electrical and Computer Engineering ◽

10.17694/bajece.14351 ◽

2016 ◽

Vol 4 (2) ◽

Cited By ~ 7

Author(s):

Seng Hansun

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Exponential Smoothing ◽

Smoothing Method ◽

New Approach ◽

Series Analysis ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Exponential Smoothing Method

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

Bulletin of the South Ural State University Ser Computer Technologies Automatic Control & Radioelectronics ◽

10.14529/ctcr210304 ◽

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.

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

Cogent Social Sciences ◽

10.1080/23311886.2018.1461544 ◽

2018 ◽

Vol 4 (1) ◽

pp. 1461544 ◽

Cited By ~ 9

Author(s):

Reindolf Anokye ◽

Enoch Acheampong ◽

Isaac Owusu ◽

Edmund Isaac Obeng ◽

Yan Lin

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Arima Models ◽

Series Analysis

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

BMC Public Health ◽

10.1186/s12889-020-09977-8 ◽

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.

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Time Series Analysis of Decadal Precipitation Pattern at Selected Cities of Southern India

Nature Environment and Pollution Technology ◽

10.46488/nept.2021.v20i03.028 ◽

2021 ◽

Vol 20 (3) ◽

Author(s):

T. S. Subbiah ◽

P. Parthiban ◽

R. Mahesh ◽

A. Das

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Empirical Analysis ◽

Stochastic Modeling ◽

Tamil Nadu ◽

Exponential Smoothing ◽

Southern India ◽

Precipitation Pattern ◽

Short Term ◽

Series Analysis

To characterize and explore the short-term climatic patterns over the last decade (Jan. 2009 to Dec. 2018), the present research has been carried out, involving time series analysis of precipitation pattern in three cities of Tamil Nadu, namely, Thanjavur, Nagapattinam, and Chennai, referring to deltaic, coastal and highly urbanized cities of Tamil Nadu, respectively. The study involves time series empirical analysis, decomposition, exponential smoothing, and various stochastic modeling. Herein, the location-specific suitable models are obtained and specific predictions are being carried out, as well.

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Price Parities for Vegetables in Serbia - Analysis and Forecasting

Contemporary Agriculture ◽

10.2478/contagri-2019-0009 ◽

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.

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Forecasting the spread of COVID19 in Hungary

10.1101/2020.11.19.20234815 ◽

2020 ◽

Author(s):

Owais Mujtaba Khanday ◽

Samad Dadvandipour ◽

Mohd. Aaqib Lone

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Reproduction Number ◽

Sir Model ◽

Exponential Smoothing ◽

Short Term ◽

Smoothing Methods ◽

Series Analysis ◽

Short Term Forecasting

AbstractTime series analysis of the COVID19/ SARS-CoV-2 spread in Hungary is presented. Different methods effective for short-term forecasting are applied to the dataset, and predictions are made for the next 20 days. Autoregression and other exponential smoothing methods are applied to the dataset. SIR model is used and predicted 64% of the population could be infected by the virus considering the whole population is susceptible to be infectious Autoregression, and exponential smoothing methods indicated there would be more than a 60% increase in the cases in the coming 20 days. The doubling of the number of total cases is found to around 16 days using an effective reproduction number.

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