An improved ARIMA model for precipitation simulations

Abstract. Auto regressive integrated moving average (ARIMA) models have been widely used to calculate monthly time series data formed by interannual variations of monthly data or inter-monthly variation. However, the influence brought about by inter-monthly variations within each year is often ignored. An improved ARIMA model is developed in this study accounting for both the interannual and inter-monthly variation. In the present approach, clustering analysis is performed first to hydrologic variable time series. The characteristics of each class are then extracted and the correlation between the hydrologic variable quantity to be predicted and characteristic quantities constructed by linear regression analysis. ARIMA models are built for predicting these characteristics of each class and the hydrologic variable monthly values of year of interest are finally predicted using the modeled values of corresponding characteristics from ARIMA model and the linear regression model. A case study is conducted to predict the monthly precipitation at the Lanzhou precipitation station in Lanzhou, China, using the model, and the results show that the accuracy of the improved model is significantly higher than the seasonal model, with the mean residual achieving 9.41 mm and the forecast accuracy increasing by 21%.

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Pengaruh Pertumbuhan Ekonomi, Tingkat Inflasi, dan Pertumbuhan Penduduk terhadap Tingkat Pengangguran Terbuka di Indonesia

Jurnal Ekonomi Akuntansi dan Manajemen ◽

10.19184/jeam.v18i1.10646 ◽

2019 ◽

Vol 18 (1) ◽

pp. 52

Author(s):

Irma Yuni Astuti ◽

Nanik Istiyani ◽

Lilis Yuliati

Keyword(s):

Economic Growth ◽

Time Series ◽

Regression Analysis ◽

Linear Regression ◽

Population Growth ◽

Multiple Linear Regression ◽

Time Series Data ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Series Data

This study aims to determine the effect of economic growth, inflation and population growth in open unemployment rate in Indonesia. The type of data used in this study is secondary data in the form of time series data and variable data used are annual data in the period 1986-2017 with the object of research in the country o Indonesia. The data sources used in this study were obtained from the Central Statistics Agency (BPS) Indonesia and World Bank. The analytical method used in this study is multiple linear regression analysis with the Ordinary Least Square (OLS) technique. The estimation of time series data with multiple linear regression analysis shows that the economics growth variable has a positive and not significant effect on the level of open unemployment, the inflation variable has a positive and not significant effect on the level of open unemployment, and the population growth variable has a negative and significant effect on the level of open unemployment in Indonesia. Keywords: Open Unemployment, Economic Growth, Inflation, Population Growth

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Time series data analysis and ARIMA modeling to forecast the short-term trajectory of the acceleration of fatalities in Brazil caused by the corona virus (COVID-19)

PeerJ ◽

10.7717/peerj.11748 ◽

2021 ◽

Vol 9 ◽

pp. e11748

Author(s):

Akini James ◽

Vrijesh Tripathi

Keyword(s):

Time Series ◽

Time Series Data ◽

Explanatory Variable ◽

Arima Model ◽

Series Data ◽

Arima Models ◽

Composite Function ◽

Short Run ◽

Lag Model ◽

Time Series Data Analysis

Objective This paper incorporates the concept of acceleration to fatalities caused by the coronavirus in Brazil from time series data beginning on 17th March 2020 (the day of the first death) to 3rd February 2021 to explain the trajectory of the fatalities for the next six months using confirmed infections as the explanatory variable. Methods Acceleration of the cases of confirmed infection and fatalities were calculated by using the concept of derivatives. Acceleration of fatality function was then determined from multivariate linear function and calculus chain rule for composite function with confirmed infections as an explanatory variable. Different ARIMA models were fitted for each acceleration of fatality function: the de-seasonalized Auto ARIMA Model, the adjusted lag model, and the auto ARIMA model with seasonality. The ARIMA models were validated. The most realistic models were selected for each function for forecasting. Finally, the short run six-month forecast was conducted on the trajectory of the acceleration of fatalities for all the selected best ARIMA models. Results It was found that the best ARIMA model for the acceleration functions were the seasonalized models. All functions suggest a general decrease in fatalities and the pace at which this change occurs will eventually slow down over the next six months. Conclusion The decreasing fatalities over the next six-month period takes into consideration the direct impact of the confirmed infections. There is an early increase in acceleration for the forecast period, which suggests an increase in daily fatalities. The acceleration eventually reduces over the six-month period which shows that fatalities will eventually decrease. This gives health officials an idea on how the fatalities will be affected in the future as the trajectory of confirmed COVID-19 infections change.

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ANALIZING FACTOR THAT AFFECTING IMPORT OF MAIZE IN INDONESIA ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI IMPOR JAGUNG DI INDONESIA

AGRIBUSINESS JOURNAL ◽

10.15408/aj.v12i2.11862 ◽

2019 ◽

Vol 12 (2) ◽

Author(s):

Raditya Audayuda ◽

Elpawati E ◽

Iwan Aminudin

Keyword(s):

Time Series ◽

Regression Analysis ◽

Linear Regression ◽

Time Series Data ◽

Linear Regression Analysis ◽

Series Data ◽

F Test ◽

Maize Production ◽

Us Dollar

The purpose of The study is to identify the factor that affecting import of maize in Indonesia, Ana to analyze The factor that affecting import of maize in Indonesia. The data used in The Study is Time series data from 1990 to 2014 sourced from BPS(Badan Pusat Statistik) and Kementrian Pertanian. The method used in The Study is linear regression analysis using SPSS 18 software. Statistics Test that used in this Study including R2 , F-test, and T-test.In this Study we can concluded that R2 test value is 0,703 that means 70,3% import of maize explained by variable that used in model, which is: maize production (produksi jagung), maize consumption (konsumsi jagung), maize domestic prize (harga jagung domestik), maize import prize (harga jagung impor), and Rupiah to US Dollar currency (Nilai tukar rupiah terhadap dollar Amerika), and remaining 29,7% remains explained by another variable that exclude by this model. After all The testing, results shows all variable in The model affecting maize import simultaneously, and partial Test shows maize production, maize consumption, maize domestic prize, and Rupiah to US Dollar currency partially affecting import of maize in Indonesia, and maize import prize variable didn’t affect import of maize in Indonesia.

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MATHEMATICAL MODELLING AS AN ELEMENT OF PLANNING RAIL TRANSPORT STRATEGIES

Transport ◽

10.3846/transport.2021.16043 ◽

2021 ◽

Vol 36 (4) ◽

pp. 354-363

Author(s):

Anna Borucka ◽

Dariusz Mazurkiewicz ◽

Eliza Łagowska

Keyword(s):

Time Series ◽

Development Strategy ◽

Time Series Data ◽

Arima Model ◽

Exponential Smoothing ◽

Rail Transport ◽

Transport Infrastructure ◽

Rolling Stock ◽

Series Data ◽

Passenger Transport

Effective planning and optimization of rail transport operations depends on effective and reliable forecasting of demand. The results of transport performance forecasts usually differ from measured values because the mathematical models used are inadequate. In response to this applicative need, we report the results of a study whose goal was to develop, on the basis of historical data, an effective mathematical model of rail passenger transport performance that would allow to make reliable forecasts of future demand for this service. Several models dedicated to this type of empirical data were proposed and selection criteria were established. The models used in the study are: the seasonal naive model, the Exponential Smoothing (ETS) model, the exponential smoothing state space model with Box–Cox transformation, ARMA errors, trigonometric trend and seasonal components (TBATS) model, and the AutoRegressive Integrated Moving Average (ARIMA) model. The proposed time series identification and forecasting methods are dedicated to the processing of time series data with trend and seasonality. Then, the best model was identified and its accuracy and effectiveness were assessed. It was noticed that investigated time series is characterized by strong seasonality and an upward trend. This information is important for planning a development strategy for rail passenger transport, because it shows that additional investments and engagement in the development of both transport infrastructure and superstructure are required to meet the existing demand. Finally, a forecast of transport performance in sequential periods of time was presented. Such forecast may significantly improve the system of scheduling train journeys and determining the level of demand for rolling stock depending on the season and the annual rise in passenger numbers, increasing the effectiveness of management of rail transport.

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Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽

10.54302/mausam.v68i2.637 ◽

2021 ◽

Vol 68 (2) ◽

pp. 349-356

Author(s):

J. HAZARIKA ◽

B. PATHAK ◽

A. N. PATOWARY

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

Demand Management ◽

Arima Model ◽

Monthly Rainfall ◽

Series Data ◽

Data Set ◽

Modeling And Forecasting ◽

Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points. We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.

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ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI IMPOR BERAS DI INDONESIA

AGRIBUSINESS JOURNAL ◽

10.15408/aj.v11i2.11843 ◽

2019 ◽

Vol 11 (2) ◽

pp. 183-201

Author(s):

Yona Namira ◽

Iskandar Andi Nuhung ◽

Mudatsir Najamuddin

Keyword(s):

Time Series ◽

Linear Regression ◽

Multiple Linear Regression ◽

Time Series Data ◽

Series Data ◽

Test Results ◽

The Us ◽

Rice Prices ◽

Us Dollar ◽

Spss Software

This study aims to 1) identify factors that affect the import of rice in Indonesia 2) analyze the influence of these factors on imports of rice in Indonesia. The data used in this research are time series data from 1994 to 2013 from the Central Statistics Agency (BPS), the Ministry of Agriculture, Ministry of Commerce, National Logistics Agency (Bulog), and Bank Indonesia. Multiple linear regression through SPSS software version 21 was employed to analyze the data. The test results together indicated the variables of productions, consumptions, stocks of rice, domestic rice prices, international rice prices and the rupiah against the US dollar affect the imports of rice in Indonesia.

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Pengaruh Industri Sedang dan Besar Terhadap Tingkat Kemiskinan di Kabupaten Gresik Tahun 2002-2016

Jurnal Ilmu Ekonomi JIE ◽

10.22219/jie.v4i1.9253 ◽

2021 ◽

Vol 4 (1) ◽

pp. 25-31

Author(s):

Rohmatul Janah ◽

Ida Nuraini

Keyword(s):

Time Series ◽

Linear Regression ◽

Multiple Linear Regression ◽

Time Series Data ◽

Industrial Sector ◽

Poverty Rate ◽

Domestic Product ◽

Series Data ◽

Insignificant Effect ◽

Poverty Levels

This research is aimed at studying the influence of medium and large industries on poverty levels in Gresik on 2002-2016. The variables used in this study is medium and large industries, a labour of medium and large industries, gross regional domestic product (GRDP) of industrial sector and poverty rate. The method used in this study used multiple linear regression and used time-series data. The results of this study simultaneously are the variables of the amount of medium and large industries, the labour medium and large industries, and the gross regional domestic product (GRDP) of the industrial sector to poverty rate is significant. While medium and large industries to poverty rate have negative and insignificant effect with a coefficient value of -0,208905. The labour of medium and large industries to poverty rate has a positive and significant effect with a coefficient value of 0,130822, the gross regional domestic product (GRDP) of industrial to poverty rate has a negative and significant effect with a coefficient value of -0,169431.

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Pengaruh pertumbuhan ekonomi, inflasi dan pengangguran terhadap tingkat kemiskinan di Sumatera Selatan

Jurnal Ekonomi Pembangunan ◽

10.29259/jep.v16i1.8856 ◽

2019 ◽

Vol 16 (1) ◽

pp. 1-10

Author(s):

Novegya Ratih Primandari

Keyword(s):

Economic Growth ◽

Time Series ◽

Linear Regression ◽

Linear Regression Model ◽

Time Series Data ◽

Secondary Data ◽

Poverty Rate ◽

Least Square ◽

Series Data ◽

Ordinary Least Square

This research aims to analyze effect of economic growth, inflation and Unemployment on the Rate of Poverty in the Province of South Sumatera. This research used secondary data in the form of time series data from 2001-2017. The method used quantitative approach by applying a linear regression model with OLS estimation Ordinary Least Square (OLS) method. The results of this study indicate that partially and simultaneously Economic Growth, Inflation and Unemployment have a significant effect on the Poverty Rate in the Province of South Sumatera.

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Market model analysis and forecasting behavior of watermelon production in Bangladesh

Bangladesh Journal of Scientific Research ◽

10.3329/bjsr.v26i1-2.20230 ◽

2014 ◽

Vol 26 (1-2) ◽

pp. 47-56

Author(s):

Murshida Khanam ◽

Umme Hafsa

Keyword(s):

Time Series ◽

Statistical Analysis ◽

Linear Models ◽

Time Series Data ◽

Arima Model ◽

Model Analysis ◽

Market Model ◽

Series Data ◽

Log Linear

An attempt has been made to study various models regarding watermelon production in Bangladesh and to identify the best model that may be used for forecasting purposes. Here, supply, log linear, ARIMA, MARMA models have been used to do a statistical analysis and forecasting behavior of production of watermelon in Bangladesh by using time series data covering whole Bangladesh. It has been found that, between the supply and log linear models; log linear is the best model. Comparing ARIMA and MARMA models it has been concluded that ARIMA model is the best for forecasting purposes. DOI: http://dx.doi.org/10.3329/bjsr.v26i1-2.20230 Bangladesh J. Sci. Res. 26(1-2): 47-56, December-2013

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Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01256-1 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Yihuai Huang ◽

Chao Xu ◽

Mengzhong Ji ◽

Wei Xiang ◽

Da He

Keyword(s):

Time Series ◽

Hybrid Model ◽

Adaptive Filtering ◽

Time Series Data ◽

Arima Model ◽

Series Data ◽

Short Term ◽

Hybrid Prediction ◽

Service Demand ◽

Filtering Method

Abstract Background Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed. Methods The ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo. Results For TSDL cases the prediction accuracy of the hybrid prediction is improved by 80–99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the MAPE predicted by the ARIMA model in the two departments was 18.53 and 27.69%, respectively, and the hybrid models were 2.79 and 1.25%, respectively. Conclusions The hybrid prediction model outperforms the traditional ARIMA model in both accurate predicting result with smaller average relative error and the applicability for short-term and medium-term prediction.

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