Mathematical modeling of traffic volume in the suburban area based on the time series decomposition

This study aims to the development of mathematical modeling methods based on time series decomposition. This method is used to describe various consistency or recurrence processes. Such a process is the distribution of traffic volume throughout the year. Its modeling is one of the leading research tasks in the transport sector. One of the urgent tasks is the assessment and forecasting of the traffic volume in the suburban areas. The study is carried out on the road section P-256 Chuysky Trakt (Novosibirsk - Barnaul - Biysk - Gorno-Altaisk -state border with Mongolia) near Biysk. Traffic data is obtained for 2019. Python is used in modelling. The statmodels module is used to decompose the time series. The multiplicative model is chosen. The adequacy of the model is checked on two groups of data. The first is the traffic volume data on the same road section for 2020. The average relative error was 5%. The second is the road section A-322 Barnaul - Rubtsovsk - the state border with the Republic of Kazakhstan in the suburban area of Aleysk. The average relative error was 6%. The results confirm the adequacy and versatility of the model.

Time series analysis for water resources management - application to observed and simulated time-series of the groundwater flow numerical model of the coastal plain of Cecina

This paper highlights the potentiality of the time series decomposition applied to transient regime groundwater flow models, as water balance management tool. In particular, this work presents results obtained by applying statistical analysis to some observed time series and to time series derived from the groundwater flow model of the coastal plain of Cecina (Tuscany region, Italy), developed in transient regime within the period 2005-2017. The time series of rainfall, river stage and hydraulic heads were firstly analysed, and then time series decomposition was applied to the “accumulated net storage”, to finally discern and quantify two meaningful components of the groundwater budget, the regulatory reserve (Wr = 22 Mm3) and the seasonal resource (Wd = 2.5 Mm3). These values compared with withdrawal volumes (average of 6.4 Mm3/y within the period 2005-2017) allowed to highlight potentially critical balance conditions, especially in periods with repeated negative climatic trends. Operational monitoring and modeling as following corrective and planning actions for the groundwater resource are suggested.

Time series decomposition into dyslipidemia prevalence among urban Chinese population: secular and seasonal trends

Background Previous epidemiological studies have indicated the seasonal variability of serum lipid levels. However, little research has explicitly examined the separate secular and seasonal trends of dyslipidemia. The present study aimed to identify secular and seasonal trends for the prevalence of dyslipidemia and the 4 clinical classifications among the urban Chinese population by time series decomposition. Methods A total of 306,335 participants with metabolic-related indicators from January 2011 to December 2017 were recruited based on routine health check-up systems. Multivariate direct standardization was used to eliminate uneven distributions of the age, sex, and BMI of participants over time. Seasonal and trend decomposition using LOESS (STL decomposition) was performed to break dyslipidemia prevalence down into trend component, seasonal component and remainder component. Results A total of 21.52 % of participants were diagnosed with dyslipidemia, and significant differences in dyslipidemia and the 4 clinical classifications were observed by sex (P <0.001). The secular trends of dyslipidemia prevalence fluctuated in 2011–2017 with the lowest point in September 2016. The dyslipidemia prevalence from January to March and May to July was higher than the annual average (λ = 1.00, 1.16, 1.06, 1.01, 1.02, 1.03), with the highest point in February. Different seasonal trends were observed among the 4 clinical classifications. Compared to females, a higher point was observed among males in February, which was similar to participants aged < 55 years (vs. ≥ 55 years) and participants with a BMI ≤ 23.9 (vs. BMI > 23.9). Conclusions There were significant secular and seasonal features for dyslipidemia prevalence among the urban Chinese population. Different seasonal trends were found in the 4 clinical classifications of dyslipidemia. Precautionary measures should be implemented to control elevated dyslipidemia prevalence in specific seasons, especially in the winter and during traditional holidays.

PERAMALAN ARUS LALU LINTAS BERDASARKAN WAKTU TEMPUH DAN CUACA MENGGUNAKAN METODE TIME SERIES DECOMPOSITION

Prediksi lalu lintas telah menjadi tren topik penelitian untuk pengembangan transportasi cerdas. Permasalahan lalu lintas pada setiap negara khususnya negara berkembang permasalahan masyarakat umum yang terjadi yaitu tidak dapat memprediksi kondisi lalu lintas di masa yang akan datang. Kondisi lalu lintas pada waktu tertentu berbeda dengan kondisi lalu lintas pada saat yang berbeda karena kebiasaan masyarakat yang berbeda pada setiap waktunya. Kondisi lalu lintas juga dipengaruhi oleh kondisi cuaca pada saat itu. Dengan mengolah data arus lalu lintas yang dijadikan faktor untuk menganalisa kondisi lalu lintas. Prediksi lalu lintas yang akurat dan efektif akan memberikan informasi arus lalu lintas sesuai kepada pengguna jalan dan efektif dalam memecahkan kepadatan arus lalu lintas. Pada penelitian ini penulis mengusulkan metode Time Series decomposition guna melakukan prediksi lalu lintas. Metode time series decomposition adalah metode peramalan dengan menggunakan analisa pola hubungan antara variable yang akan diperkirakan dengan variable waktu. Secara umum time series mengalisa 4 pola data yaitu pola trend, seasonal, pola horizontal dan pola siklis. Data yang hitung pada penelitian yaitu data waktu tempuh perjalan dan kondisi cuaca. Metode prediksi time series decomposition diterapkan pada pada aplikasi mobile berbasis android agar pengguna dapat mengetahui informasi prediksi melalui smartphone. Hasil dari penelitian ini menghasilkan prediksi dengan nilai error RMSE sebesar 3.80%. Hasil tersebut membuktikan bahwa metode time series decomposition dapat digunakan untuk membantu prediksi lalu lintas.

A Novel Hybrid Deep Learning Model for Sugar Price Forecasting Based on Time Series Decomposition

Sugar price forecasting has attracted extensive attention from policymakers due to its significant impact on people’s daily lives and markets. In this paper, we present a novel hybrid deep learning model that utilizes the merit of a time series decomposition technology empirical mode decomposition (EMD) and a hyperparameter optimization algorithm Tree of Parzen Estimators (TPEs) for sugar price forecasting. The effectiveness of the proposed model was implemented in a case study with the price of London Sugar Futures. Two experiments are conducted to verify the superiority of the EMD and TPE. Moreover, the specific effects of EMD and TPE are analyzed by the DM test and improvement percentage. Finally, empirical results demonstrate that the proposed hybrid model outperforms other models.