scholarly journals Time series forecasting using ARIMA for modeling of glioma growth in response to radiotherapy

2021 ◽  
Vol 42 (1) ◽  
pp. 3
Author(s):  
Larissa Miguez da Silva ◽  
Gustavo Benitez Alvarez ◽  
Eliane da Silva Christo ◽  
Gerardo Amado Pelén Sierra ◽  
Vanessa da Silva Garcia
Keyword(s):  
Time Series ◽  
Mathematical Models ◽  
Moving Average ◽  
Primary Tumors ◽  
Autoregressive Integrated Moving Average ◽  
Glioma Growth ◽  
The Central Nervous System ◽  
Time Series Method ◽  
Treatment Dosage ◽  
Radiotherapy Treatment

In present days, the growing number of people suffering from cancer has been a major cause for concern worldwide. Glioblastoma in particular, are primary tumors in glial cells located in the central nervous system. Because of this sensitive location, mathematical models have been studied and developed as alternative tools for analyzing tumor growth rates, assisting on the decision-making process for treatment dosage, without exposing the patient’s life. This paper presents two time series models to estimate the growth rate of glioblastoma in response to ionizing radiotherapy treatment. The results obtained indicate that the proposed time series methods attain predictions with a Mean Absolute Percentual Error (MAPE) of approximately 1% to 4%, and simulations show that the Autoregressive Integrated Moving Average (ARIMA) method surpasses the Holt method based on the Mean Square Error (MSE) and MAPE values obtained. Furthermore, the results show that the time series method is applicable to data from two different mathematical models for glioblastoma growth.

scholarly journals PERAMALAN PERSEDIAAN INFUS MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) PADA RUMAH SAKIT UMUM PUSAT SANGLAH

2018 ◽  
Vol 7 (2) ◽  
pp. 129
Author(s):  
I PUTU YUDI PRABHADIKA ◽  
NI KETUT TARI TASTRAWATI ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Time Series ◽  
General Hospital ◽  
Moving Average ◽  
Glucose Infusion ◽  
Series Method ◽  
Autoregressive Integrated Moving Average ◽  
Important Thing ◽  
Forecasting Method ◽  
Time Series Method ◽  
The Many

Infusion supplies are an important thing that must be considered by the hospital in meeting the needs of patients. This study aims to predict the need for infusion of 0.9% 500 ml of NaCl and 5% 500 ml glucose infusion at Sanglah General Hospital (RSUP) Sanglah so that the hospital can estimate the many infusions needed for the next six months. The forecasting method used in this research is the autoregressive integrated moving average (ARIMA) time series method. The results of this study indicate the need for infusion at Sanglah Hospital as many as 154,831 units for infusion of 0.9% NaCl 500 ml and 8,249 units for 5% 500 ml Glucose infusion.

scholarly journals Application of the Autoregressive Integrated Moving Average Exogenous (ARIMAX) with Calendar Variation Effect Method for Forecasting Chocolate Data in Indonesia and the United States

2022 ◽  
Vol 18 (2) ◽  
pp. 224-236
Author(s):  
Andy Rezky Pratama Syam
Keyword(s):  
United States ◽  
Time Series ◽  
Moving Average ◽  
The United States ◽  
Time Series Regression ◽  
Autoregressive Integrated Moving Average ◽  
Good Ability ◽  
Modeling And Forecasting ◽  
Time Series Method ◽  
Double Exponential Smoothing

Forecasting chocolate consumption is required by producers in preparing the amount of production each month. The tradition of Valentine, Christmas and Eid al-Fitr which are closely related to chocolate makes it impossible to predict chocolate by using the Classical Time Series method. Especially for Eid al-Fitr, the determination follows the Hijri calendar and each year advances 10 days on the Masehi calendar, so that every three years Eid al-Fitr will occur in a different month. Based on this, the chocolate forecasting will show a variation calendar effect. The method used in modeling and forecasting chocolate in Indonesia and the United States is the ARIMAX (Autoregressive Integrated Moving Average Exogenous) method with Calendar Variation effect. As a comparison, modeling and forecasting are also carried out using the Naïve Trend Linear, Naïve Trend Exponential, Double Exponential Smoothing, Time Series Regression, and ARIMA methods. The ARIMAX method with Calendar Variation Effect produces a very precise MAPE value in predicting chocolate data in Indonesia and the United States. The resulting MAPE value is below 10 percent, so it can be concluded that this method has a very good ability in forecasting.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

scholarly journals A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 141
Author(s):  
Jacob Hale ◽  
Suzanna Long
Keyword(s):  
Time Series ◽  
Electricity Generation ◽  
Sustainability Assessment ◽  
Moving Average ◽  
Time Series Prediction ◽  
State Level ◽  
Autoregressive Integrated Moving Average ◽  
Energy Transitions ◽  
Univariate Time Series ◽  
Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

scholarly journals Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
C. W. Tian ◽  
H. Wang ◽  
X. M. Luo
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Moving Average ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Annual Peak ◽  
Time Series Modelling ◽  
Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

PERAMALAN TINGKAT PENGANGGURAN DI INDONESIA MENGGUNAKAN METODE TIME SERIES DENGAN MODEL ARIMA DAN HOLT-WINTERS

2021 ◽  
Vol 26 (1) ◽  
pp. 13-28
Author(s):  
Agus Sulaiman ◽  
Asep Juarna
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Model Time Series

Beberapa penyebab terjadinya pengangguran di Indonesia ialah, tingkat urbanisasi, tingkat industrialisasi, proporsi angkatan kerja SLTA dan upah minimum provinsi. Faktor-faktor tersebut turut serta mempengaruhi persentase data terkait tingkat pengangguran menjadi sedikit fluktuatif. Berdasarkan pergerakan persentase data tersebut, diperlukan sebuah prediksi untuk mengetahui persentase tingkat pengangguran di masa depan dengan menggunakan konsep peramalan. Pada penelitian ini, peneliti melakukan analisis peramalan time series menggunakan metode Box-Jenkins dengan model Autoregressive Integrated Moving Average (ARIMA) dan metode Exponential Smoothing dengan model Holt-Winters. Pada penelitian ini, peramalan dilakukan dengan menggunakan dataset tingkat pengangguran dari tahun 2005 hingga 2019 per 6 bulan antara Februari hingga Agustus. Peneliti akan melihat evaluasi Range Mean Square Error (RMSE) dan Mean Square Error (MSE) terkecil dari setiap model time series. Berdasarkan hasil penelitian, ARIMA(0,1,12) menjadi model yang terbaik untuk metode Box-Jenkins sedangkan Holt-Winters dengan alpha(mean) = 0.3 dan beta(trend) = 0.4 menjadi yang terbaik pada metode Exponential Smoothing. Pemilihan model terbaik dilanjutkan dengan perbandingan nilai akurasi RMSE dan MSE. Pada model ARIMA(0,1,12) nilai RMSE = 1.01 dan MSE = 1.0201, sedangkan model Holt-Winters menghasilkan nilai RMSE = 0.45 dan MSE = 0.2025. Berdasarkan data tersebut terpilih model Holt-Winters sebagai model terbaik untuk peramalan data tingkat pengangguran di Indonesia.

scholarly journals Time-Series Statistical Model for Forecasting Revenue and Risk Management

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Mihir Kelkar ◽  
Cosmin Borsa ◽  
Lina Kim
Keyword(s):  
Time Series ◽  
Low Cost ◽  
Moving Average ◽  
Business Strategies ◽  
Annual Reports ◽  
Fiscal Year ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Low Cost Carrier ◽  
Southwest Airlines

Following a Low-Cost Carrier (LCC) model, Southwest Airlines has consistently demonstrated growing annual revenues up until the start of the COVID-19 pandemic. Southwest’s quarterly revenue shows that there exists a strong seasonal component with the revenue in the first quarter of the fiscal year (September) significantly higher than other quarters. Using the quarterly revenue data we constructed a time-series model: a seasonal autoregressive integrated moving average (SARIMA) model to forecast Southwest’s revenue over 2020. We then performed a cost and solvency risk analysis using the company’s financial results from its annual reports to analyze Southwest’s financial performance due to COVID-19, and proposed business strategies to keep Southwest financially stable.

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