scholarly journals Time series modelling of increased soil temperature anomalies during long period

2015 ◽  
Vol 29 (4) ◽  
pp. 509-515 ◽  
Author(s):  
Amin Shirvani ◽  
Farzad Moradi ◽  
Ali Akbar Moosavi
Keyword(s):  
Soil Temperature ◽  
Linear Trend ◽  
Moving Average ◽  
Soil Parameters ◽  
Plant Seed ◽  
Significance Level ◽  
Autoregressive Integrated Moving Average ◽  
Temperature Anomalies ◽  
Proposed Model ◽  
Time Series Modelling

AbstractSoil temperature just beneath the soil surface is highly dynamic and has a direct impact on plant seed germination and is probably the most distinct and recognisable factor governing emergence. Autoregressive integrated moving average as a stochastic model was developed to predict the weekly soil temperature anomalies at 10 cm depth, one of the most important soil parameters. The weekly soil temperature anomalies for the periods of January1986-December 2011 and January 2012-December 2013 were taken into consideration to construct and test autoregressive integrated moving average models. The proposed model autoregressive integrated moving average (2,1,1) had a minimum value of Akaike information criterion and its estimated coefficients were different from zero at 5% significance level. The prediction of the weekly soil temperature anomalies during the test period using this proposed model indicated a high correlation coefficient between the observed and predicted data - that was 0.99 for lead time 1 week. Linear trend analysis indicated that the soil temperature anomalies warmed up significantly by 1.8°C during the period of 1986-2011.

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.

scholarly journals Social mobility and gonorrhoea in Germany during 2020

2021 ◽  
pp. sextrans-2021-055159
Author(s):  
Mihály Sulyok ◽  
Mark Walker
Keyword(s):  
Time Series ◽  
Health Services ◽  
Infectious Diseases ◽  
Social Mobility ◽  
Social Factors ◽  
Moving Average ◽  
Social Activity ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
The Relationship

ObjectivesThe incidence of STIs is likely to be related to levels of social activity and mobility. Novel datasets detailing levels of social activity were made widely available during the COVID-19 pandemic. These allow the relationship between activity and STI incidence to be examined.MethodsThe correlation between social activities and the reported number of gonorrhoea cases between March and December 2020 in Germany was studied. Regression through Autoregressive Integrated Moving Average (ARIMA) time series modelling identified those activities associated with case numbers.ResultsARIMA regression identified a significant association with ‘transit’ activity within the Apple data and ‘parks’ within Google.ConclusionsThis study illustrates the potential newly available measures of social activity provided for STI research. Reductions in STI incidence are likely to have occurred due to COVID-19 social restrictions. Although other studies report reductions in infectious diseases during this period, few examine the potential social factors mediating this. The results illustrate the continual need for sexual health services throughout the pandemic.

A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting

2017 ◽  
Vol 8 (4) ◽  
pp. 30-53 ◽  
Author(s):  
Warut Pannakkong ◽  
Van-Hai Pham ◽  
Van-Nam Huynh
Keyword(s):  
Hybrid Model ◽  
Forecast Error ◽  
Moving Average ◽  
Data Sets ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Hybrid Forecasting ◽  
Us Dollar ◽  
Different Levels ◽  
Prediction Capability

This article aims to propose a novel hybrid forecasting model involving autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means clustering. The single models and k-means clustering are used to build the hybrid forecasting models in different levels of complexity (i.e. ARIMA; hybrid model of ARIMA and ANNs; and hybrid model of k-means, ARIMA, and ANN). To obtain the final forecasting value, the forecasted values of these three models are combined with the weights generated from the discount mean square forecast error (DMSFE) method. The proposed model is applied to three well-known data sets: Wolf's sunspot, Canadian lynx and the exchange rate (British pound to US dollar) to evaluate the prediction capability in three measures (i.e. MSE, MAE, and MAPE). In addition, the prediction performance of the proposed model is compared to ARIMA; ANNs; Khashei and Bijari's model; and the hybrid model of k-means, ARIMA, and ANN. The obtained results show that the proposed model gives the best performance in MSE, MAE, and MAPE for all three data sets.

scholarly journals Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices

2016 ◽  
Vol 8 (3) ◽  
pp. 15
Author(s):  
Kesaobaka Molebatsi ◽  
Mpho Raboloko
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
Error Terms ◽  
Autoregressive Conditional Heteroscedasticity

<p>This paper identifies an autoregressive integrated moving average (ARIMA (1,1,1)) model that can be used to model inflation measured by the consumer price index (CPI) for Botswana. The paper proceeds to improve the model by incorporating the generalized autoregressive conditional heteroscedasticity (ARCH/GARCH) model that takes into consideration volatility in the series. Ultimately, CPI is forecast using the two models, ARIMA (1, 1, 1) and ARIMA (1, 1, 1) + GARCH (1, 2) and compared with the actual CPI. Both models perform well in terms of forecasting as their 95 percent confidence intervals cover the actual CPI. Marginal differences that favour the inclusion of the ARCH/GARCH components were observed when testing for normality among error terms. The paper also reveals that volatility for Botswana’s CPI is low as shown by small values of ARCH/GARCH components.</p>

scholarly journals Online SARIMA applied for short-term electricity load forecasting

2021 ◽  
Author(s):  
Quang Dat Nguyen ◽  
Nhat Anh Nguyen ◽  
Ngoc Thang Tran ◽  
Vijender Kumar Solanki ◽  
Rubén González Crespo ◽  
...  
Keyword(s):  
Power Systems ◽  
Moving Average ◽  
Supply And Demand ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Short Term Load Forecasting ◽  
Electricity Load

Abstract Short-term Load Forecasting (STLF) plays a crucial role in balancing supply and demand of load dispatching operation, ensures stability for the power system. With the advancement of real-time smart sensors in power systems, it is of great significance to develop techniques to handle data streams on-the-fly to improve operational efficiency. In this paper, we propose an online variant of Seasonal Autoregressive Integrated Moving Average (SARIMA) to forecast electricity load sequentially. The proposed model is utilized to forecast hourly electricity load of northern Vietnam and achieves a mean absolute percentage error (MAPE) of 4.57%.

A Forecasting Model for Patient Arrivals of an Emergency Department in Healthcare Management Systems

2019 ◽  
pp. 266-284 ◽  
Author(s):  
Melih Yucesan ◽  
Muhammet Gul ◽  
Suleyman Mete ◽  
Erkan Celik
Keyword(s):  
Neural Network ◽  
Human Resources ◽  
Medical Devices ◽  
Moving Average ◽  
Healthcare Management ◽  
Management Systems ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Artificial Neural Network Ann

Emergency departments (EDs) are one of the most valuable departments of healthcare management systems. Patient arrivals at the EDs are crucial for planning of the future. Accurate forecasting of patient arrivals contributes to better organized human resources and medical devices in the EDs. Therefore, in this chapter, the authors aim to develop a hybrid model including the methods of autoregressive integrated moving average with external variables (ARIMAX) and artificial neural network (ANN) in a hospital ED. The arrival data was collected from the hospital information system of a public hospital in eastern Turkey. The model incorporates factors related to ED arrivals such as climatic and calendar variables. By the aid of the proposed model, an insight to arrangement and planning of ED resources can be provided in a better way.

scholarly journals Forecasting Time Series Movement Direction with Hybrid Methodology

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Salwa Waeto ◽  
Khanchit Chuarkham ◽  
Arthit Intarasit
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Movement Direction ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Hybrid Methodology

Forecasting the tendencies of time series is a challenging task which gives better understanding. The purpose of this paper is to present the hybrid model of support vector regression associated with Autoregressive Integrated Moving Average which is formulated by hybrid methodology. The proposed model is more convenient for practical usage. The tendencies modeling of time series for Thailand’s south insurgency is of interest in this research article. The empirical results using the time series of monthly number of deaths, injuries, and incidents for Thailand’s south insurgency indicate that the proposed hybrid model is an effective way to construct an estimated hybrid model which is better than the classical time series model or support vector regression. The best forecast accuracy is performed by using mean square error.

scholarly journals Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

2020 ◽  
pp. 101-110
Author(s):  
Evizal Abdul Kadir ◽  
Nur Ezzati Dayana ◽  
Sri Listia Rosa ◽  
Mahmod Othman ◽  
Rizauddin Saian
Keyword(s):  
Time Series ◽  
Forest Fires ◽  
Mean Squared Error ◽  
Moving Average ◽  
Arima Model ◽  
Forest Damage ◽  
Autoregressive Integrated Moving Average ◽  
Squared Error ◽  
Proposed Model ◽  
Forecasting System

Various forms of disasters occur worldwide, one of which is fire. Indonesia has been suffering from frequent land and forest fires. These events are not a new phenomenon and seem to be an annual tradition, especially in the dry season. This nation was most affected by an excessively disastrous forest fire in 2015. The misfortunes suffered were massive and resulted in land and forest damage that may have great economic and environmental costs. One solution to reduce the impacts of such events is to predict the emergence of hotspots. Therefore, in this work, a modeling method using time series produced by the Box-Jenkins' Autoregressive Integrated Moving Average (ARIMA) model was used to predict the appearance of hotspots. Since the forecasting system does not expect any detailed form to be predicted in terms of the time series of historical data, the data demonstrated in the proposed model were different from data from other models used for prediction. The study was conducted based on monthly hotspot occurrence data from January 2014 through June 2019 in Riau Province, Indonesia. The data were downloaded from the collection of the "LAPAN-MODIS-Catalog". Based on the results shown, the Autoregressive Integrated Moving Average (ARIMA) model (2,1,2) produced good predictions based on its lowest value of Mean Squared Error (MSE), 9540.088. Moreover, the proposed model has produced highly accurate forecasts of hotspots for time periods of up to five months using the fitting model of ARIMA (2,1,2), and the values forecasted for 5 months ahead were 25, 31, 26, 30 and 27.

scholarly journals Comparing of ARIMA and RBFNN for short-term forecasting

2015 ◽  
Vol 1 (1) ◽  
pp. 15 ◽  
Author(s):  
Haviluddin Haviluddin ◽  
Ahmad Jawahir
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Short Term Forecasting ◽  
Simulated Time ◽  
Modelling And Forecasting

Based on a combination of an autoregressive integrated moving average (ARIMA) and a radial basis function neural network (RBFNN), a time-series forecasting model is proposed. The proposed model has examined using simulated time series data of tourist arrival to Indonesia recently published by BPS Indonesia. The results demonstrate that the proposed RBFNN is more competent in modelling and forecasting time series than an ARIMA model which is indicated by mean square error (MSE) values. Based on the results obtained, RBFNN model is recommended as an alternative to existing method because it has a simple structure and can produce reasonable forecasts.

scholarly journals Forecasting Confirmed Cases and Mortalities of COVID-19 in the US

2020 ◽  
Author(s):  
Babak Jamshidi ◽  
Mohsen Kakavandi ◽  
Shahriar Jamshidi Zargaran ◽  
Amir Talaei-Khoei
Keyword(s):  
Time Series ◽  
Reproductive Strategies ◽  
Moving Average ◽  
Large Body ◽  
Cumulative Number ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
Moving Average Models ◽  
The Us ◽  
Time Point

AbstractBackgroundThe wide spread of COVID-19 in the US has placed the country as the most infected population worldwide. This paper aims to forecast the number of confirmed cases and mortalities from 12 April to 21 May, 2020. There has been a large body of literature in forecasting epidemic outbreaks such as C algorithms with shortfall of predicting for long periods and autoregressive integrated moving average models with the limited flexibility. However, the US COVID-19 data shows great variety in the relative increments of confirmed cases. This requires a reproductive time series.MethodThis paper suggests a time series based on the relative increments of confirmed cases. The proposed time series assumes the changes in the time series and provides flexibility. The suggested model was applied on the data observed from 27 February to 11 April 2020 and its objective is forecasting 40 days from 12 April to 21 May 2020.ResultsIt is expected that by May 21, 2020, the accumulative number of confirmed cases of COVID-19 in the US rises to 1,464,729, with 80% confidence interval. Our analysis also shows that by the 21st of May, the cumulative number of mortalities caused by COVID-19 in the US from 18747 cases on 11 April increases to around 73250 cases on 21 May, 2020.ConclusionOur results highlight the value of reproductive strategies in time series modelling of COVID-19. Our model benefits from a reproductive strategy from a time point in which the US COVID-19 data demonstrates a sudden fall.

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