Time series air quality forecasting with R Language and R Studio

Setiawan, Irwan

doi:10.1088/1742-6596/1450/1/012064

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The ets() algorithm refers to a family of exponential smoothing models that are flexible in modelling different time patterns, including trends and seasonality [78,79]. Despite the fact that the exponential smoothing of time series is one of the traditional methods, it is further used for forecasting in business contexts due to the fact that the created forecasts can further produce adequate results [80,81].…”

Section: Description Of the Predictive Algorithmmentioning

confidence: 99%

Enhancing Smart Cities through Third-Party Logistics: Predicting Delivery Intensity

Kmiecik,

Wierzbicka

2024

Smart Cities

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This article addresses the key and current issues of smart cities in the context of last-mile supply management. Specifically, it explores how third-party logistics (3PL) activities impact last-mile delivery management in smart cities. It examines how 3PL affects delivery volumes, expanding the predictive capabilities of logistics operators. A research question included in the Introduction of this paper is also posed to explore the problem in depth. The research conducted focuses mainly on a case study conducted on the operations of an international 3PL logistics operator. In addition, predictive methods are used to analyse the shipment volume data for individual barcodes in the two analysed cities in Poland. Currently, the concept of a smart city assumes the limited participation of logistics operators in creating improvements for cities. The case study analysis shows that in the cities studied, 3PL companies, through predictive actions, can regulate the flow of vehicles out of the logistics centre and into the city, thus influencing the traffic volume in the city. The research is limited to two cities in Poland implementing smart city solutions and one logistics operator. The research also does not include e-commerce. The authors acknowledge that the results obtained cannot be generalised to a larger scale. This paper bridges the research gap on 3PL activities for last-mile logistics improvements. In addition, the paper proposes the first concept related to the implementation of a 3PL company’s predictive activities associated with the operator’s ability to control the impact on urban traffic.

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Section: Description Of the Predictive Algorithmmentioning

confidence: 99%

Enhancing Smart Cities through Third-Party Logistics: Predicting Delivery Intensity

Kmiecik,

Wierzbicka

2024

Smart Cities

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“…Data pre-processing: this is the second step of the EDA process. This process performs data integration (such as finding redundant attributes and tuple duplication and inconsistency), data cleaning, imputation of missing values [24], dealing with noisy data, and data reduction [25].…”

Section: Figure 1 Eda Stepsmentioning

confidence: 99%

Exploratory data analysis of crime report

Setiawan

Suprihanto

2021

MATRIX

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Visualization of data is the appearance of data in a pictographic or graphical form. This form facilitates top management to understand the data visually and get the messages of difficult concepts or identify new patterns. The approach of the personal understanding to handle data; applying diagrams or graphs to reflect vast volumes of complex data is more comfortable than presenting over tables or statements. In this study, we conduct data processing and data visualization for crime report data that occurred in the city of Los Angeles in the range of 2010 to 2017 using R language. The research methodology follows five steps, namely: variables identification, data pre-processing, univariate analysis, bivariate analysis, and multivariate analysis. This paper analyses data related to crime variables, time of occurrence, victims, type of crime, weapons used, distribution, and trends of crime, and the relationship between these variables. As the result shows, by using those methods, we can gain insights, understandings, new patterns, and do visual analytics from the existing data. The variations of crime variables presented in this paper are only a few of the many variations that can be made. Other variations can be performed to get more insights, understandings, and new patterns from the existing data. The methods can be performed on other types of data as well.

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“…Time series forecasting regarding PM2.5 has also been carried out in many previous studies, such as [5][6][7][8]. Time series data is data that is observed based on time sequences with the same range (hours, days, months, and so on).…”

Section: Introductionmentioning

confidence: 99%

Predicting particulate matter PM2.5 using the exponential smoothing and Seasonal ARIMA with R studio

Amelia

Guskarnali

Mahardika

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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In general, public awareness of air quality in Indonesia is increasing. In accordance with the average concentration of particular PM2.5, air quality in Indonesia has improved from 2020 to 2021. However, in some densely populated cities, poor air quality still occurs continuously, for example Jakarta. PM2.5 pollution prediction will be made using monthly data with a case study Jakarta using the time series method, Exponential Smoothing and Seasonal ARIMA model in R studio. In accordance with the analysis, it is found that the Triple Exponential Smoothing and Seasonal ARIMA (0,1,1)(1,1,1)(12) were chosen to be the selected models. When compared to the actual data, they fluctuate move following the actual data. However, when viewed from the average percentage difference with the actual data, the model whose data is close to the actual data is the Seasonal ARIMA model. It is hoped that by knowing the PM2.5 particulate time series model in Jakarta, it can be used as consideration for predicting the monthly average PM2.5 so that it can be seen which months have the highest PM2.5. So that some people who experience respiratory problems, can predict the highest PM2.5 condition and can anticipate early in case of unhealthy air conditions.

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Time series air quality forecasting with R Language and R Studio

Cited by 5 publications

References 6 publications

Enhancing Smart Cities through Third-Party Logistics: Predicting Delivery Intensity

Enhancing Smart Cities through Third-Party Logistics: Predicting Delivery Intensity

Exploratory data analysis of crime report

Predicting particulate matter PM2.5 using the exponential smoothing and Seasonal ARIMA with R studio

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