scholarly journals Time Series Analysis of the Pollutants Imissions in Urban Areas

1970 ◽  
Vol 66 (2) ◽  
Author(s):  
Carmen Leane NICOLESCU ◽  
Daniel DUNEA ◽  
Virgil MOISE ◽  
Gabriel GORGHIU
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Urban Areas ◽  
Linear Trend ◽  
Moving Average ◽  
Quality Parameters ◽  
Temporal And Spatial Distribution ◽  
Simple Exponential Smoothing ◽  
Environmental Air ◽  
Local Agencies

Environmental pollution of urban areas is one of the key factors that local agencies and authorities have to consider in the decision-making process. To succeed a sustainable management of the environment, there is necessary to use different kinds of instruments in order to evaluate and forecast the evolution of the environmental state. Understanding temporal and spatial distribution of air quality is essential in making decisions for regional management. In this paper a model for urban air quality forecasting using time series of monthly averages concentrations is presented. Sedimentable dusts (SD), total suspended particulates (TSP), nitrogen dioxide (NO2), and sulfur dioxide (SO2), imissions, recorded between 1995 and 2008 in the urban area of Târgovişte city are used as inputs in the model. The measured pollutant data from the local Environmental Agency database were statistically analyzed in time series including monthly patterns using the auto-regressive integrated moving average (ARIMA) method, linear trend, simple moving average of three terms and simple exponential smoothing. There was discussed the efficiency of using this method in forecasting the environmental air quality. In general, ARIMA technique scores well in predicting the analysed environmental air quality parameters.

scholarly journals Explicit Modeling of Meteorological Explanatory Variables in Short-Term Forecasting of Maximum Ozone Concentrations via a Multiple Regression Time Series Framework

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1304
Author(s):  
Sigfrido Iglesias-Gonzalez ◽  
Maria E. Huertas-Bolanos ◽  
Ivan Y. Hernandez-Paniagua ◽  
Alberto Mendoza
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Multiple Regression ◽  
Urban Areas ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Air Pollutant ◽  
Short Term ◽  
Ozone Level ◽  
Ozone Concentrations

Statistical time series forecasting is a useful tool for predicting air pollutant concentrations in urban areas, especially in emerging economies, where the capacity to implement comprehensive air quality models is limited. In this study, a general multiple regression with seasonal autoregressive moving average errors model was estimated and implemented to forecast maximum ozone concentrations with a short time resolution: overnight, morning, afternoon and evening. In contrast to a number of short-term air quality time series forecasting applications, the model was designed to explicitly include the effects of meteorological variables on the ozone level as exogenous variables. As the application location, the model was constructed with data from five monitoring stations in the Monterrey Metropolitan Area of Mexico. The results show that, together with structural stochastic components, meteorological parameters have a significant contribution for obtaining reliable forecasts. The resulting model is an interpretable, useful and easily implementable model for forecasting ozone maxima. Moreover, it proved to be consistent with the general dynamics of ozone formation and provides a suitable platform for forecasting, showing similar or better performance compared to models in other existing studies.

A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Air Quality in Urban Areas: Case of Tunisia

2012 ◽  
Vol 518-523 ◽  
pp. 2969-2979 ◽  
Author(s):  
Ayari Samia ◽  
Nouira Kaouther ◽  
Trabelsi Abdelwahed
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Urban Areas ◽  
Moving Average ◽  
Warning System ◽  
Model Combining ◽  
Ann Models ◽  
Artificial Neural

Forecasting air quality time series represents a very difficult task since air quality contains autoregressive, linear and nonlinear patterns. Autoregressive Integrated Moving Average (ARIMA) models have been widely used in air quality time series forecasting. However, they fail to detect extreme events because of their presumed linear form of data. Artificial Neural Networks (ANN) models have proved to be promising nonlinear tools for air quality forecasting. A hybrid model combining ARIMA and ANN improved forecasting more than either of the models used independently. Experimental results with meteorological and Particulate Matter data indicated that the combined model can be used as an efficient forecasting and early warning system for providing air quality information towards the citizen, not only in Sfax Southern Suburbs but in other Tunisian regions that suffer from poor air quality conditions.

Load Forecasting using Time Series Techniques

2021 ◽  
pp. 11343-11357
Author(s):  
Shahida Khatoon, Ibraheem, Priti, Mohammad Shahid
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Linear Trend ◽  
Moving Average ◽  
Load Forecasting ◽  
Electric Load ◽  
Time Data ◽  
Efficient Operation ◽  
Use Of Time ◽  
Distribution Company

Load Forecasting is of great significance for effective and efficient operation of power system. Use of time series is of much importance in load forecasting. In this study, effectiveness of different time series techniques is identified to gathered valuable information. The objective is to predict electric load efficiently and effectively. This paper analyses the prediction accuracy of variety of time series method in modeling Electric load forecasts. The study examines the time series forecasting methods applied to estimate future electric load, specifically, Moving Average (MA), Linear Trend, the Exponential and Parabolic Trend. A comparison of different forecasting techniques of Time Series is demonstrated on real time data. The data utilized for forecast is made available through a distribution company of India. The traditional linear models and hybrid models along with ANN are developed. These models are appraised for the forecasting capability.

scholarly journals Technical note: Decomposing a time series into independent trend, seasonal and random components

2018 ◽  
Author(s):  
Dongqin Yin ◽  
Hannah Slatford ◽  
Michael L. Roderick
Keyword(s):  
Time Series ◽  
Traditional Approach ◽  
Linear Trend ◽  
Moving Average ◽  
Climatic Variability ◽  
Original Data ◽  
Technical Note ◽  
Decomposition Approach ◽  
Random Components ◽  
Original Time

Abstract. Many time series observations in hydrology and climate show large seasonal variations and it has long been common practice to separate the original data into trend, seasonal and random components. We were interested in using that decomposition approach as a basis for understanding variability in hydro-climatic time series. For that purpose, it is desirable that the trend, seasonal and random components are independent so that the variance of the original time series equals the sum of the variances of the three components. We show that the resulting decomposition with the trend component traditionally estimated either as a linear trend or a moving average does not produce components that are independent. Instead we introduce the rarely adopted two-way ANOVA model into studies of hydro-climatic variability and define the trend as equal to the annual anomaly. This traditional approach produces a decomposition with three independent components. We then use global land precipitation data to demonstrate a simple application showing how this decomposition method can be used as a basis for comparing hydro-climatic variability. We anticipate that the three-part decomposition based on the two-way ANOVA approach will prove useful for future applications that seek to understand the space-time dimensions of hydro-climatic variability.

scholarly journals Forecasting With Exponential Smoothing Whats The Right Smoothing Constant?

2013 ◽  
Vol 17 (3) ◽  
pp. 117-126 ◽  
Author(s):  
Handanhal V. Ravinder
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Error Measures ◽  
Nonlinear Trend ◽  
Simple Exponential Smoothing ◽  
The Right ◽  
Numerous Time

This paper examines exponential smoothing constants that minimize summary error measures associated with a large number of forecasts. These forecasts were made on numerous time series generated through simulation on a spreadsheet. The series varied in length and underlying nature no trend, linear trend, and nonlinear trend. Forecasts were made using simple exponential smoothing as well as exponential smoothing with trend correction and with different kinds of initial forecasts. We found that when initial forecasts were good and the nature of the underlying data did not change, smoothing constants were typically very small. Conversely, large smoothing constants indicated a change in the nature of the underlying data or the use of an inappropriate forecasting model. These results reduce the confusion about the role and right size of these constants and offer clear recommendations on how they should be discussed in classroom settings.

scholarly journals The Global Case-Fatality Rate of COVID-19 Has Been Declining Since May 2020

2021 ◽  
Author(s):  
Mohammad Nayeem Hasan ◽  
Najmul Haider ◽  
Florian L. Stigler ◽  
Rumi Ahmed Khan ◽  
David McCoy ◽  
...  
Keyword(s):  
Time Series ◽  
Case Fatality Rate ◽  
Moving Average ◽  
Case Fatality ◽  
Mortality Data ◽  
Fatality Rate ◽  
Peak Period ◽  
Prevalence Of Obesity ◽  
Simple Exponential Smoothing ◽  
Rate Ratios

The objective of this study was to evaluate the trend of reported case fatality rate (rCFR) of COVID-19 over time, using globally reported COVID-19 cases and mortality data. We collected daily COVID-19 diagnoses and mortality data from the WHO’s daily situation reports dated January 1 to December 31, 2020. We performed three time-series models [simple exponential smoothing, auto-regressive integrated moving average, and automatic forecasting time-series (Prophet)] to identify the global trend of rCFR for COVID-19. We used beta regression models to investigate the association between the rCFR and potential predictors of each country and reported incidence rate ratios (IRRs) of each variable. The weekly global cumulative COVID-19 rCFR reached a peak at 7.23% during the 17th week (April 22–28, 2020). We found a positive and increasing trend for global daily rCFR values of COVID-19 until the 17th week (pre-peak period) and then a strong declining trend up until the 53rd week (post-peak period) toward 2.2% (December 29–31, 2020). In pre-peak of rCFR, the percentage of people aged 65 and above and the prevalence of obesity were significantly associated with the COVID-19 rCFR. The declining trend of global COVID-19 rCFR was not merely because of increased COVID-19 testing, because COVID-19 tests per 1,000 population had poor predictive value. Decreasing rCFR could be explained by an increased rate of infection in younger people or by the improvement of health care management, shielding from infection, and/or repurposing of several drugs that had shown a beneficial effect on reducing fatality because of COVID-19.

scholarly journals Predicting Total Business Sales using Time Series Analysis

2020 ◽  
pp. 475-482
Author(s):  
Navya Sri Kalli ◽  
Harsha Teja Pullagura
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Time Series Models ◽  
Key Factors ◽  
Sales Data ◽  
Grand Total ◽  
Simple Exponential Smoothing ◽  
Total Sales

aEconomic activity undergoes 4 phases (expansion, peak, contraction, trough/recession) in which recession is a period of lowest activity and peak indicates the highest activity. Total Business sales is one of the key factors that influence the economic activity of a country. Total sales or gross sales is the grand total of all sales revenues a business generates from normal activities. The frequency of time series sales data can be monthly, quarterly, or annually. Prediction of business sales is highly important as it determines various factors in the market including Gross Domestic Product (GDP). The algorithms or models required for prediction of time series data are different from other machine learning models. Since sales is affected by time, a time series data should be stationary. Only when the data is stationarized, we can apply the algorithms on them. In this paper, monthly sales data is collected and predictions are done using moving average, simple exponential smoothing, Holt’s model, ARIMA, and SARIMAX. Root Mean Square(RMS) is the accuracy metric of time series models and lower RMS indicates higher accuracy. In this paper, a lower value of RMS is obtained for the SARIMAX model.

scholarly journals Identifying an Appropriate Forecasting Technique for Predicting Future Demand: A Case Study on a Private University of Bangladesh

2018 ◽  
Vol 66 (1) ◽  
pp. 15-19
Author(s):  
Sayma Suraiya ◽  
M Babul Hasan
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Moving Average ◽  
Demand Forecasting ◽  
Private University ◽  
Planning Horizon ◽  
Time Series Forecasting ◽  
Base Level ◽  
Printing Paper

Demand forecasting and inventory control of printing paper is crucial that is frequently used every day for the different purposes in all sectors of educational area especially in Universities. A case study is conducted in a University store house to collect all historical demand data of printing papers for last 6 years (18 trimesters), from January (Spring) 2011 to December (Fall) 2016. We will use the different models of time series forecasting which always offers a steady base-level forecast and is good at handling regular demand patterns. The aim of the research paper is to find out the less and best error free forecasting techniques for the demand of printing paper for a particular time being by using the quantitative forecasting or time series forecasting models like weighted moving average, 3-point single moving average, 3-point double moving average, 5-point moving average, exponential smoothing, regression analysis/linear trend, Holt’s method and Winter’s method. According to the forecasting error measurement, we will observe in this research that the best forecasting technique is linear trend model. By using the quantities of data and drawing the conclusion with an acceptable accuracy, our analysis will help the university to decide how much inventory is absolutely needed for the planning horizon. Dhaka Univ. J. Sci. 66(1): 15-19, 2018 (January)

scholarly journals Application of the "SIX SIGMA" method for the analysis of the improvement of the environmental air quality parameters at the municipality of Bucharest, by monitoring the pollutances of NOX pollutants

2020 ◽  
Author(s):  
Ion Durbaca ◽  
Nicoleta Sporea ◽  
Dana-Claudia Farcas-Flamaropol ◽  
Elena Surdu
Keyword(s):  
Air Quality ◽  
Six Sigma ◽  
Harmful Effect ◽  
Ground Level ◽  
Ambient Air ◽  
Quality Parameters ◽  
Maximum Efficiency ◽  
Ambient Air Quality ◽  
High Concentrations ◽  
Environmental Air

This paper analyzes the improvement of ambient air quality indicators by monitoring the NOx concentration in one of the most polluted areas of Bucharest, using the statistical method "SIX SIGMA" (6σ). By applying the methodology of this statistical approach, the aim is to reduce non-conformities within the specified limits (according to the standards and legislative norms in force) and respectively, to ensure maximum efficiency (99,99%), equivalent to a yield of 3.4 defects per million opportunities (DPMO). As high concentrations of air pollutants have a major impact on human health, the most harmful effect has been found to be caused by nitrogen dioxide (NO2), mainly from ground-level ozone. Using the "6σ" method, the optimal solutions for eliminating non-conformities and implicitly for reducing the NO2 concentration and ensuring the improvement of the ambient air quality can be identified.

scholarly journals Assessment of integrated indoor environmental air quality parameters in selected church buildings of Faisalabad city: a statistical based comparative study

2021 ◽  
Vol 30 (1) ◽  
pp. 134-147
Author(s):  
Zahid Hussain ◽  
Kashif Alaf ◽  
Muhammad Khan ◽  
Hamza Kundi ◽  
Kashif Ullah
Keyword(s):  
Air Quality ◽  
Relative Humidity ◽  
Air Temperature ◽  
Negative Relationship ◽  
Strong Relationship ◽  
Well Being ◽  
Quality Parameters ◽  
Church Building ◽  
Environmental Air ◽  
Church Buildings

The objective of this study is to control the air quality parameters for a selected range of different particulate matters. A comprehensive experimental approach is established to regulate the quality of air about a selected range of different air pollutants being investigated in the indoor atmosphere of the church building. Relative humidity, temperature, carbon dioxide, particulate matter and radon were considered as the factors of air quality extents. For establishing the association among the selected parameters, the data were mathematically analyzed. The correlation coefficient confirmed a strong relationship between the indoor CO2 level and the number of public. A negative relationship between the indoor CO2 extent and indoor temperature confirmed that due to the increase in temperature the concentration of CO2 decreased as well. A solid adverse connection among indoor relative humidity and indoor air temperature showed that due to the increase in air temperature, the level of the relative humidity decreased. Some recommendations were proposed for the treatment of air quality in church buildings for human well-being.

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