Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study

ObjectivesHaemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China.DesignTime-series study.SettingThe People’s Republic of China.MethodsAutoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model were constructed by R V.3.4.3 software. The monthly reported incidence of HFRS from January 2011 to May 2018 were adopted to evaluate models’ performance. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were adopted to evaluate these models’ effectiveness. Spatial stratified heterogeneity of the time series was tested by month and another GRNN model was built with a new series.ResultsThe monthly incidence of HFRS in the past several years showed a slight downtrend and obvious seasonal variation. A total of four plausible ARIMA models were built and ARIMA(2,1,1) (2,1,1)12model was selected as the optimal model in HFRS fitting. The smooth factors of the basic GRNN model and the hybrid model were 0.027 and 0.043, respectively. The single ARIMA model was the best in fitting part (MAPE=9.1154, MAE=89.0302, RMSE=138.8356) while the hybrid model was the best in prediction (MAPE=17.8335, MAE=152.3013, RMSE=196.4682). GRNN model was revised by building model with new series and the forecasting performance of revised model (MAPE=17.6095, MAE=163.8000, RMSE=169.4751) was better than original GRNN model (MAPE=19.2029, MAE=177.0356, RMSE=202.1684).ConclusionsThe hybrid ARIMA-GRNN model was better than single ARIMA and basic GRNN model in forecasting monthly incidence of HFRS in China. It could be considered as a decision-making tool in HFRS prevention and control.

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Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

BMJ Open ◽

10.1136/bmjopen-2020-039676 ◽

2020 ◽

Vol 10 (12) ◽

pp. e039676

Author(s):

Mirxat Alim ◽

Guo-Hua Ye ◽

Peng Guan ◽

De-Sheng Huang ◽

Bao-Sen Zhou ◽

...

Keyword(s):

Time Series ◽

Arima Model ◽

Mainland China ◽

Human Brucellosis ◽

Training Set ◽

Test Set ◽

Time Series Study ◽

Extreme Gradient Boosting ◽

Series Study ◽

Monthly Incidence

ObjectivesHuman brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In this study, a comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more suitable for predicting the occurrence of brucellosis in mainland China.DesignTime-series study.SettingMainland China.MethodsData on human brucellosis in mainland China were provided by the National Health and Family Planning Commission of China. The data were divided into a training set and a test set. The training set was composed of the monthly incidence of human brucellosis in mainland China from January 2008 to June 2018, and the test set was composed of the monthly incidence from July 2018 to June 2019. The mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the effects of model fitting and prediction.ResultsThe number of human brucellosis patients in mainland China increased from 30 002 in 2008 to 40 328 in 2018. There was an increasing trend and obvious seasonal distribution in the original time series. For the training set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 338.867, 450.223 and 10.323, respectively, and the MAE, RSME and MAPE of the XGBoost model were 189.332, 262.458 and 4.475, respectively. For the test set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 529.406, 586.059 and 17.676, respectively, and the MAE, RSME and MAPE of the XGBoost model were 249.307, 280.645 and 7.643, respectively.ConclusionsThe performance of the XGBoost model was better than that of the ARIMA model. The XGBoost model is more suitable for prediction cases of human brucellosis in mainland China.

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Tuberculosis forecasting and temporal trends by gender and age in a high endemic city of Northeast Brazil: Are we achieving the disease elimination stage?

10.21203/rs.3.rs-147043/v1 ◽

2021 ◽

Author(s):

Hamilton Leandro Andrade ◽

Luiz Arroyo ◽

Antônio Carlos Ramos ◽

Marcelino Neto ◽

Melina Yamamura ◽

...

Keyword(s):

Time Series ◽

Temporal Trend ◽

Moving Average ◽

Arima Model ◽

Temporal Trends ◽

Northeast Brazil ◽

Age Group ◽

Time Series Study ◽

Gender And Age ◽

Series Study

Abstract Objective: to describe the temporal trend of tuberculosis cases according to gender and age group and to make forecasts in an endemic municipality of northeast Brazil. Method: This was a Time Series study, carried out in a municipality in the northeast of Brazil. Population was composed of tuberculosis cases among residents of the municipality, reported between the years 2002 and 2018. An exploratory analysis of the monthly rates of tuberculosis detection, smoothed according to gender and age group, was performed. Subsequently, the progression of the trend and predictions of the disease were also characterized according to these aspects. For the trends forecast, the seasonal autoregressive linear integrated moving average – Seasonal ARIMA model and the usual Box-Jenkins method were used to choose the most appropriate models.Results: A total of 1,620 cases of tuberculosis were reported, with an incidence of 49.7 cases per 100,000 inhabitants in men and 34.0 per 100,000 in women. Regarding the incidence for both genders, there was a decreasing trend, which was similar for age. Evidence resulting from the application of the time series shows a decreasing trend between the years 2002–2018, however, it is unlikely that there will be a significant fall in the disease before 2022.

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Hybrid Neural Models For Rice Yields Times Forecasting

Jurnal Teknologi ◽

10.11113/jt.v52.128 ◽

2012 ◽

Author(s):

Ruhaidah Samsudin ◽

Puteh Saad ◽

Ani Shabri

Keyword(s):

Time Series ◽

Hybrid Model ◽

Moving Average ◽

Time Series Prediction ◽

Arima Model ◽

Data Sets ◽

Ann Model ◽

Yield Data ◽

Rice Yields ◽

Artificial Neural Network Ann

In this paper, time series prediction is considered as a problem of missing value. A model for the determination of the missing time series value is presented. The hybrid model integrating autoregressive intergrated moving average (ARIMA) and artificial neural network (ANN) model is developed to solve this problem. The developed models attempts to incorporate the linear characteristics of an ARIMA model and nonlinear patterns of ANN to create a hybrid model. In this study, time series modeling of rice yield data in Muda Irrigation area. Malaysia from 1995 to 2003 are considered. Experimental results with rice yields data sets indicate that the hybrid model improve the forecasting performance by either of the models used separately. Key words: ARIMA; Box and Jenkins; neural networks; rice yields; hybrid ANN model

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The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

International Research in Higher Education ◽

10.5430/irhe.v4n3p58 ◽

2019 ◽

Vol 4 (3) ◽

pp. 58

Author(s):

Lu Qin ◽

Kyle Shanks ◽

Glenn Allen Phillips ◽

Daphne Bernard

Keyword(s):

Time Series ◽

Historical Data ◽

Moving Average ◽

Arima Model ◽

Forecasting Accuracy ◽

Autoregressive Integrated Moving Average ◽

Enrollment Forecasting ◽

Moving Average Model ◽

The Impact ◽

Practical Impact

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

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The Forecasting of the Elevator Traffic Flow Time Series Based on ARIMA and GP

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.588-589.1466 ◽

2012 ◽

Vol 588-589 ◽

pp. 1466-1471 ◽

Cited By ~ 1

Author(s):

Jun Fang Li ◽

Qun Zong

Keyword(s):

Time Series ◽

Traffic Flow ◽

Linear Models ◽

Moving Average ◽

Arima Model ◽

Flow Time ◽

Forecasting Model ◽

Autoregressive Integrated Moving Average ◽

Hybrid Forecasting ◽

Artificial Neural Network Ann

As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

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SHORT-TERM TRAFFIC FLOW PREDICTION USING A METHODOLOGY BASED ON AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND GENETIC PROGRAMMING

Transport ◽

10.3846/16484142.2016.1212734 ◽

2016 ◽

Vol 31 (3) ◽

pp. 343-358 ◽

Cited By ~ 18

Author(s):

Chengcheng Xu ◽

Zhibin Li ◽

Wei Wang

Keyword(s):

Genetic Programming ◽

Traffic Flow ◽

Hybrid Model ◽

Moving Average ◽

Arima Model ◽

Predictive Performance ◽

Hybrid Models ◽

Short Term ◽

Time Intervals ◽

Autoregressive Integrated Moving Average

The accurate short-term traffic flow forecasting is fundamental to both theoretical and empirical aspects of intelligent transportation systems deployment. This study aimed to develop a simple and effective hybrid model for forecasting traffic volume that combines the AutoRegressive Integrated Moving Average (ARIMA) and the Genetic Programming (GP) models. By combining different models, different aspects of the underlying patterns of traffic flow could be captured. The ARIMA model was used to model the linear component of the traffic flow time series. Then the GP model was applied to capture the nonlinear component by modelling the residuals from the ARIMA model. The hybrid models were fitted for four different time-aggregations: 5, 10, 15, and 20 min. The validations of the proposed hybrid methodology were performed by using traffic data under both typical and atypical conditions from multiple locations on the I-880N freeway in the United States. The results indicated that the hybrid models had better predictive performance than utilizing only ARIMA model for different aggregation time intervals under typical conditions. The Mean Relative Error (MRE) of the hybrid models was found to be from 4.1 to 6.9% for different aggregation time intervals under typical conditions. The predictive performance of the hybrid method was improved with an increase in the aggregation time interval. In addition, the validation results showed that the predictive performance of the hybrid model was also better than that of the ARIMA model under atypical conditions.

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Forecasting Sugarcane yield of India using ARIMA-ANN hybrid model

Bhartiya Krishi Anusandhan Patrika ◽

10.18805/bkap100 ◽

2018 ◽

Vol 33 (01) ◽

Author(s):

Mrinmoy Ray ◽

R. S. Tomar ◽

Ramasubramanian V. ◽

K. N. Singh

Keyword(s):

Hybrid Model ◽

Moving Average ◽

Arima Model ◽

Main Stream ◽

Research Activity ◽

Forecasting Accuracy ◽

Autoregressive Integrated Moving Average ◽

Cash Crops ◽

Sugarcane Yield ◽

The Individual

Sugarcane is one of the main cash crops of India hence forecasting sugarcane yield is vital for proper planning. Till date Autoregressive integrated moving average (ARIMA) model is a stand out amongst the most main stream approach for sugarcane yield forecasting. Recent research activity reveals that hybrid model improves the accuracy of forecasting when contrasted with the individual model. Along these lines, in this study, ARIMA-ANN hybrid model was utilized for forecasting sugarcane yield of India. The hybrid model was compared with ARIMA approach. Empirical results clearly reveal that the forecasting accuracy of the hybrid model is superior to ARIMA.

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Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling – Neural Network

Asian Journal of Agricultural Extension Economics & Sociology ◽

10.9734/ajaees/2019/v30i230106 ◽

2019 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Debasis Mithiya ◽

Lakshmikanta Datta ◽

Kumarjit Mandal

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Time Series Forecasting ◽

Series Data ◽

Percentage Error ◽

Group Method ◽

Data Handling ◽

Agricultural Economy ◽

Autoregressive Integrated Moving Average

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

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PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN ◽

10.30598/barekengvol13iss3pp135-144ar883 ◽

2019 ◽

Vol 13 (3) ◽

pp. 135-144

Author(s):

Sasmita Hayoto ◽

Yopi Andry Lesnussa ◽

Henry W. M. Patty ◽

Ronald John Djami

Keyword(s):

Time Series ◽

Model Selection ◽

Time Series Data ◽

Moving Average ◽

Arima Model ◽

Series Data ◽

Autoregressive Integrated Moving Average ◽

Significant Parameter ◽

International Airport ◽

Forecast Time

The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

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Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

Jambura Journal of Mathematics ◽

10.34312/jjom.v1i1.8430 ◽

2021 ◽

Vol 1 (1) ◽

pp. 52-65

Author(s):

Drajat Indra Purnama

Keyword(s):

Support Vector Regression ◽

Hybrid Model ◽

Moving Average ◽

Arima Model ◽

Training Data ◽

Series Data ◽

Support Vector ◽

Autoregressive Integrated Moving Average ◽

Testing Data ◽

Linearity Test

ABSTRAKInvestasi emas merupakan salah satu investasi yang menjadi favorit dimasa pandemi Covid 19 seperti sekarang ini. Hal ini dikarenakan harga emas yang nilainya relatif fluktuatif tetapi menunjukkan tren peningkatan. Investor dituntut pandai dalam berinvestasi emas, mampu memprediksi peluang dimasa yang akan datang. Salah satu model peramalan data deret waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model ARIMA baik digunakan pada data yang berpola linear tetapi jika digunakan pada data data nonlinear keakuratannya menurun. Untuk mengatasi permasalahan data nonlinear dapat menggunakan model Support Vector Regression (SVR). Pengujian linearitas pada data harga emas menunjukkan adanya pola data linear dan nonlinear sekaligus sehingga digunakan kombinasi ARIMA dan SVR yaitu model hybrid ARIMA-SVR. Hasil peramalan menggunakan model hybrid ARIMA-SVR menunjukkan hasil lebih baik dibanding model ARIMA. Hal ini dibuktikan dengan nilai MAPE model hybrid ARIMA-SVR lebih kecil dibandingkan nilai MAPE model ARIMA. Nilai MAPE model hybrid ARIMA-SVR sebesar 0,355 pada data training dan 4,001 pada data testing, sedangkan nilai MAPE model ARIMA sebesar 0,903 pada data training dan 4,076 pada data testing.ABSTRACTGold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

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