Identification of Time Series Models of Car Traffic Volume Fluctuations for Further Construction of Prediction Models

Purpose – Online advertising such as Google AdWords gives small and medium-sized enterprises access to new markets at reduced costs. The purpose of this paper is to analyse the visibility and performance of a website and to test the effectiveness of online marketing using the data provided by Google Analytics. Design/methodology/approach – The authors use a class of econometric time series models with unobservable components, Structural Time Series Models (STSM). The authors allow for time-varying trends to take into account the non-stationary behaviour displayed by time series. The authors illustrate the model using daily data from a local tourist website. Three specific questions are addressed: do paid keywords campaigns increase the volume and quality of search traffic? Do paid keywords affect the volume and quality of the unpaid traffic? How do paid and unpaid keywords perform? Findings – The results for the case study show that: first, online campaigns affect traffic volume positively but their effectiveness on traffic quality is uncertain; second, paid keywords do not affect the volume and quality of unpaid traffic; third, the increase in traffic volume is not always due to the paid keywords and the lowest quality visits come from paid traffic. Practical implications – This analysis may help webmasters to design successful online advertising strategies. Originality/value – This study contributes to the development of user-friendly methodologies to monitor website performance. The analysis shows that STSM is a suitable methodology to test the effectiveness of online campaigns and to assess the changes over time in the performance of a website.

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FORECASTING AIR PASSENGER TRAFFIC VOLUME: EVALUATING TIME SERIES MODELS IN LONG-TERM FORECASTING OF KUWAIT AIR PASSENGER DATA

Advances and Applications in Statistics ◽

10.17654/as070010069 ◽

2021 ◽

Vol 70 (1) ◽

pp. 69-89

Author(s):

Ahmad T. Al-Sultan ◽

Amani Al-Rubkhi ◽

Ahmad Alsaber ◽

Jiazhu Pan

Keyword(s):

Time Series ◽

Traffic Volume ◽

Time Series Models ◽

Passenger Traffic ◽

Long Term Forecasting

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FEATURES OF TIME SERIES USING FOR ESTIMATION OF FLUCTUATIONS OF CAR TRAFFIC VOLUMES AT THE RAILWAY DIVISION POINTS

T-Comm ◽

10.36724/2072-8735-2021-15-1-35-40 ◽

2021 ◽

Vol 15 (1) ◽

pp. 35-40

Author(s):

Ekaterina V. Malovetskaya ◽

◽

Roman S. Bolshakov ◽

Keyword(s):

Time Series ◽

Predictive Models ◽

Transport Process ◽

Prediction Models ◽

Far East ◽

Transport Activity ◽

Car Traffic ◽

The Far East ◽

Traffic Volumes ◽

The Russian Federation

The study of intra-annual dynamics of car traffic volumes, handed over at the railway division points of the Russian Federation is an essential part of the long-range prediction, planning and analysis of transport activity. The increase in the regularity of pace of the production process is directly related to the assessment of the unevenness of car traffic volumes. The ability to predict the unevenness of the transport process, as well as uneven loading with the establishment of relevant indicators is a key issue in the regularity of pace of transport operation. The presented article analyzes the structure of time series of fluctuations in car traffic volumes at the railway division points in order to further build a model for predicting fluctuations in car traffic volumes and, in the future, loading cargo to the ports of the Far East. This methodology is based on the analysis of the structure of time series of fluctuations in car traffic volumes handed over at the railway division points and moving further towards seaports with the subsequent construction of a mathematical model of cargo loading, on the basis of which it will subsequently be possible to predict the loading for the coming year. The presented work considers an analysis of the structure of time series of fluctuations in car traffic volumes and proposes models for the subsequent construction of a prediction. It also applies a systemic approach to solving the problem of predicting the car traffic volume. The proposed tools make it possible to develop prediction models to assess the seasonal unevenness of cargo loading in the direction of seaports. All this will contribute to the improvement of logistics planning of transportation and will give a further impetus to the development of the industry. The whole range of activities consists in the possibility of constructing predictive models for the production unit of the Russian Railways holding. In addition, it will be possible to update the structure of the network’s operational indicators.

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Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China

Infectious Diseases of Poverty ◽

10.1186/s40249-020-00771-7 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Zhong-Qi Li ◽

Hong-Qiu Pan ◽

Qiao Liu ◽

Huan Song ◽

Jian-Ming Wang

Keyword(s):

Time Series ◽

Pulmonary Tuberculosis ◽

Prediction Models ◽

Meteorological Factors ◽

Eastern China ◽

Arima Model ◽

Rank Correlation ◽

Percentage Error ◽

Time Series Models ◽

Cross Correlation Analysis

Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.

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Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting

Algorithms ◽

10.3390/a13020039 ◽

2020 ◽

Vol 13 (2) ◽

pp. 39 ◽

Cited By ~ 1

Author(s):

Pavlyuk

Keyword(s):

Time Series ◽

Transfer Learning ◽

Prediction Models ◽

Urban Traffic ◽

Modern Concept ◽

Time Series Models ◽

Traffic Data ◽

Data Set ◽

Traffic Forecasting ◽

Video Prediction

Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area. In this paper, we identify links between methodologies in two fields: video prediction and spatiotemporal traffic forecasting. The similarities of the video stream and citywide traffic data structures are discovered and analogues between historical development and modern states of the methodologies are presented and discussed. The idea of transferring video prediction models to the urban traffic forecasting domain is validated using a large real-world traffic data set. The list of transferred techniques includes spatial filtering by predefined kernels in combination with time series models and spectral graph convolutional artificial neural networks. The obtained models’ forecasting performance is compared to the baseline traffic forecasting models: non-spatial time series models and spatially regularized vector autoregression models. We conclude that the application of video prediction models and algorithms for urban traffic forecasting is effective both in terms of observed forecasting accuracy and development, and training efforts. Finally, we discuss problems and obstacles of transferring methodologies and present potential directions for further research.

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The Forecast of the Cargo Transportation and Traffic Volume on Container in Gwangyang Port, using Time Series Models

Journal of Navigation and Port Research ◽

10.5394/kinpr.2008.32.6.425 ◽

2008 ◽

Vol 32 (6) ◽

pp. 425-431 ◽

Cited By ~ 1

Author(s):

Jung-Hoon Kim

Keyword(s):

Time Series ◽

Traffic Volume ◽

Time Series Models ◽

Cargo Transportation

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Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽

10.15358/0344-1369-2010-jrm-1-24 ◽

2010 ◽

Vol 32 (JRM 1) ◽

pp. 24-29

Author(s):

Marnik G. Dekimpe ◽

Dominique M. Hanssens

Keyword(s):

Time Series ◽

Time Series Models ◽

Recent Developments

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A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i1.6760 ◽

2020 ◽

Vol 5 (1) ◽

pp. 374

Author(s):

Pauline Jin Wee Mah ◽

Nur Nadhirah Nanyan

Keyword(s):

Time Series ◽

Palm Oil ◽

Moving Average ◽

Forecast Accuracy ◽

Peninsular Malaysia ◽

Time Series Models ◽

Crude Palm Oil ◽

Univariate Time Series ◽

Import And Export ◽

Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA emerged the best model based on the RMSE value. When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

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