Time Series Analysis Using ARIMA Model for Air Pollution Prediction in Hyderabad City of India

2021 ◽  
pp. 47-56
Author(s):  
Pooja Gopu ◽  
Rama Ranjan Panda ◽  
Naresh Kumar Nagwani
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Time Series Analysis ◽  
Arima Model ◽  
Series Analysis ◽  
Air Pollution Prediction ◽  
Pollution Prediction

scholarly journals Forecasting of Economic Indicators (Production, Consumption, Population) of Wheat Crop (A Case Study)

2021 ◽  
Vol 8 (6) ◽  
pp. 38-46
Author(s):  
Mohammad Karim Ahmadzai
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Wheat Crop ◽  
Series Analysis ◽  
The People ◽  
Self Sufficiency ◽  
Univariate Time Series ◽  
Short Run

Wheat is the most important food crop in Afghanistan, whether consumed by the bulk of the people or used in various sectors. The problem is that Afghanistan has a significant shortfall of wheat between domestic production and consumption. Thus, the present study looks at the issue of meeting self-sufficiency for the whole population due to wheat shortages. To do so, we employ time series analysis, which can produce a highly exact short-run prediction for a significant quantity of data on the variables in question. The ARIMA models are versatile and widely utilised in univariate time series analysis. The ARIMA model combines three processes: I the auto-regressive (AR) process, (ii) the differencing process, and (iii) the moving average (MA) process. These processes are referred to as primary univariate time series models in statistical literature and are widely employed in various applications. Where predicting future wheat requirements is one of the most important tools that decision-makers may use to assess wheat requirements and then design measures to close the gap between supply and consumption. The present study seeks to forecast Production, Consumption, and Population for the period 2002-2017 and estimate the values of these variables between 2002 and 2017. (2018-2030).  

Time series analysis of ambient air pollution effects on dynamic stroke mortality

2019 ◽  
Vol 35 (1) ◽  
pp. 79-103 ◽  
Author(s):  
Li Luo ◽  
Yuting Dai ◽  
Fengyi Zhang ◽  
Mei Chen ◽  
Fang Chen ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Time Series Analysis ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Stroke Mortality ◽  
Pollution Effects ◽  
Series Analysis ◽  
Air Pollution Effects
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