Statistical Modeling and forecasting of weather Data Distribution Using Improved Time Series Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 141-154
Author(s):  
Afrah Al-Bossly
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Statistical Modeling ◽  
Data Distribution ◽  
Weather Data ◽  
Series Analysis ◽  
Modeling And Forecasting

scholarly journals Association between weather factors and dengue in Dhaka: a time-series analysis

2021 ◽  
Vol 39 (1) ◽  
pp. 43-49
Author(s):  
Shafia Shaheen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Incidence Rate ◽  
Negative Binomial ◽  
Weather Data ◽  
Negative Binomial Regression Model ◽  
Weather Factors ◽  
Average Temperature ◽  
Series Analysis ◽  
Average Maximum

Background: There was an epidemic of dengue fever that happened  in Bangladesh  in the year of 2019. Temperature of this country has been raising which leads to changing in rainfall pattern. This study was aimed to investigate the relationship of weather factors and dengue incidence in Dhaka. Methods: A time series analysis was carried out by using 10 years weather data as average , maximum and minimum monthly temperature, average monthly humidity and average and cumulative monthly rainfall. Reported number of dengue cases was extracted from January 2009 to July 2019. Firstly, dengue incidence rate was  calculated. Correlation analysis and negative binomial regression model was developed. Results: Dengue incidence rate had sharp upward trend. Dengue incidence and mean, maximum and minimum average temperature showed statistically significant negative correlation at 3 months' lag. Highest incidence Rate Ratio (IRR) of dengue was found at minimum average temperature at 0 and I-month lag. Average humidity showed positive and significant correlation with dengue incidence at 0-month lag. Average and cumulative rainfall also showed negative and significant correlation only at 3-months lag period. Conclusion: Weather variability influences dengue incidence and the association between the weather factors are non­ linear and not consistent. So the study findings should be evaluated area basis with other local factors to develop early warning for dengue epidemic prediction. JOPSOM 2020; 39(1): 43-49

Modeling and Forecasting Sales Data by Time Series Analysis

1981 ◽  
Vol 18 (1) ◽  
pp. 94 ◽  
Author(s):  
S. G. Kapoor ◽  
P. Madhok ◽  
S. M. Wu
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Sales Data ◽  
Series Analysis ◽  
Modeling And Forecasting

scholarly journals Modeling and forecasting of the under-five mortality rate in Kermanshah province in Iran: a time series analysis

2015 ◽  
Vol 37 ◽  
pp. e2015003 ◽  
Author(s):  
Mehran Rostami ◽  
Abdollah Jalilian ◽  
Behrooz Hamzeh ◽  
Zahra Laghaei
Keyword(s):  
Time Series ◽  
Mortality Rate ◽  
Time Series Analysis ◽  
Under Five ◽  
Series Analysis ◽  
Modeling And Forecasting
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