Analysis of water budget prediction accuracy using ARIMA models

2017 ◽  
Vol 18 (3) ◽  
pp. 819-830 ◽  
Author(s):  
M. Birylo ◽  
Z. Rzepecka ◽  
J. Kuczynska-Siehien ◽  
J. Nastula
Keyword(s):  
Groundwater Level ◽  
Land Surface ◽  
Water Budget ◽  
Prediction Accuracy ◽  
Moving Average ◽  
The European Union ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Run Off ◽  
Satisfactory Prediction

Abstract The European Union Water Framework Directive obliges each country to monitor the groundwater level as it is an important source of drinking water, but also an important part of agriculture. A water budget is used for assessing the accuracy of the groundwater level determination. The computations of the water budget are based on evapotranspiration and the state of land surface hydrosphere. On the basis of the determined water budget, statistics and the prognosis for the next 12 months can be computed. In this paper, all the components of the water budget, such as precipitation, surface run-off and evapotranspiration, are studied for the three tested locations in Poland: Suwalki, Zegrzynski and Tarnow cells. The resultant water budget was also determined and presented graphically. On the basis of the water budget research, a prognosis was determined using AutoRegressive Integrated Moving Average (ARIMA) models with the parameters (2,0,2). A comparison between actual water budget data and a prediction prepared for 2015.08–2016.08 indicated that analysing a 12-month period provides a satisfactory prediction assessment.

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

scholarly journals Forecasting Short-Term Timber Prices with Univariate ARIMA Models

1999 ◽  
Vol 23 (1) ◽  
pp. 53-58 ◽  
Author(s):  
Runsheng Yin
Keyword(s):  
Moving Average ◽  
Arima Models ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Price Series ◽  
Modeling Strategy

Abstract In this paper, we conduct timber price forecasts with univariate autoregressive-integrated-moving-average, or ARIMA, models employing the standard Box-Jenkins modeling strategy. Using quarterly price series from Timber Mart-South, we find that most of the selected pine pulpwood and sawtimber markets can be evaluated using ARIMA models, and that short-term forecasts, especially those of one-lead forecasts, are fairly accurate. We believe that forecasting future prices could aid timber producers and consumers alike in timing harvests, reducing uncertainty, and enhancing efficiency. South. J. Appl. For. 23(1):53-58.

scholarly journals Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Rahul Tripathi ◽  
A. K. Nayak ◽  
R. Raja ◽  
Mohammad Shahid ◽  
Anjani Kumar ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Selection Criterion ◽  
Information Criteria ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Rice Productivity ◽  
Rice Area ◽  
Moving Average Models ◽  
Partial Autocorrelation Function

Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA) models and was compared with the forecasted all Indian data. The autoregressive (p) and moving average (q) parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF) and autocorrelation function (ACF) of the different time series. ARIMA (2, 1, 0) model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1) was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC) and Schwarz-Bayesian information criteria (SBC). The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE), which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.

scholarly journals ARIMA Time Series Models for Full Truckload Transportation Prices

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 121-134 ◽  
Author(s):  
Jason W. Miller
Keyword(s):  
Time Series ◽  
Moving Average ◽  
The United States ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Estimated Parameters ◽  
Coefficients Of Variation ◽  
Labor Statistics ◽  
Bureau Of Labor Statistics ◽  
Practical Implications

The trucking sector in the United States is a $700 billion plus a year industry and represents a large percentage of many firms’ logistics spend. Consequently, there is interest in accurately forecasting prices for truck transportation. This manuscript utilizes the autoregressive integrated moving average (ARIMA) methodology to develop forecasts for three time series of monthly archival trucking prices obtained from two public sources—the Bureau of Labor Statistics (BLS) and Truckstop.com. BLS data cover January 2005 through August 2018; Truckstop.com data cover January 2015 through August 2018. Different ARIMA models closely approximate the observed data, with coefficients of variation of the root mean-square deviations being 0.007, 0.040, and 0.048. Furthermore, the estimated parameters map well onto dynamics known to operate in the industry, especially for data collected by the BLS. Theoretical and practical implications of these findings are discussed.

scholarly journals Using Autoregressive Integrated Moving Average (ARIMA) Technique to Forecast the Production of Kharif Cereals in Odisha (India)

2020 ◽  
pp. 104-113
Author(s):  
Abhiram Dash ◽  
A. Mangaraju ◽  
Pradeep Mishra ◽  
H. Nayak
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Percentage Error ◽  
Arima Models ◽  
Training Set ◽  
Autoregressive Integrated Moving Average ◽  
Future Production ◽  
Best Fit ◽  
Testing Set

Cereals are the most important kharif season crop in Odisha. The present study was carried out to forecast the production of kharif cereals in Odisha by using the forecast values of area and yield of kharif cereals obtained from the selected best fit Autoregressive Integrated Moving Average (ARIMA) model. The data from 1970-71 to 2010-11 are considered as training set data and used for model building and from 2011-12 to 2015-16 are considered as testing set data and used for cross-validation of the selected model on the basis of the absolute percentage error. The ARIMA models are fitted to the stationary data which may be the original data or the differenced data. The different ARIMA models are evaluated on the basis of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) at various lags. The possible ARIMA models are selected on the basis of significant coefficient of autoregressive and moving average components by using the training set data. The best fitted models are then selected on the basis of residual diagnostics test and model fit statistics. The ARIMA model found to be best fitted for area under kharif cereals and yield of kharif cereals are ARIMA (1,1,0) without constant and ARIMA (0,1,2) without constant respectively which are successfully cross-validated with the testing set data. The respective best fit ARIMA model has been used to forecast the area and yield of kharif cereals for the years 2016-17, 2017-18 and 2018-19. The forecast values of area shows a decrease, whereas, the forecast values of yield shows an increase. The decrease in area might have been the result of limited availability of area for cereals due to shifting towards non-food grain crops. The forecast values of production of kharif cereals obtained from the forecast values of area and yield of kharif cereals shows an increase which is due to the increase in forecast values of yield. Since there is limited scope for area expansion, the future production of kharif cereals can only be increased by increasing the yield to achieve the goal of food security for the growing population.

scholarly journals Epidemic Potential for Human Infection with Influenza A (H7N9) Virus in China through Web Search Behaviors: A Data-Driven Study

2017 ◽  
Author(s):  
Yue Teng ◽  
Dehua Bi ◽  
Xiaocan Guo ◽  
Dan Feng ◽  
Yigang Tong
Keyword(s):  
Influenza A ◽  
Moving Average ◽  
H7n9 Virus ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Search Data ◽  
H7n9 Influenza ◽  
Search Index ◽  
Shanghai Municipality

AbstractSince the beginning of September 2016, a steep upsurge of the human cases of avian influenza A (H7N9) virus has been reported in China, which are alarming public concern for the pandemic potential of the H7N9 virus. In this study, we collected the data from H7N9 epidemics and H7N9-related Baidu Search Index (BSI) in China between September 2013 and June 2017. And we observed a strong correlation between the numbers of Influenza A (H7N9) cases and H7N9-related BSI in Guangdong province and Shanghai municipality (p<0.001). Autoregressive integrated moving average (ARIMA) models were constructed for the dynamic estimation of seasonal H7N9 outbreaks in 2016-2017 and the online search data acted as an external regressor with the historical H7N9 epidemic data in the forecasting model to improve the quality of predictions. Predictions by the models closely matched the actual numbers of reported cases during current H7N9 epidemic season. Especially, the estimated numbers of reported cases sharply increased to reach 49.88 (95% CI: 0-194.05) in Guangdong and 9.05 (95% CI: 0-37.43) in Shanghai from December 2016 to June 2017. Moreover, this accessible and flexible dynamic forecast model could be used in the monitoring of H7N9 virus to provide advanced warning of future emerging infection diseases.Author summaryAs the availability and popularity of the internet has greatly increased in recent years, an increasing number of cyber users, including patients and their family members, search online for health information on personal computers (PCs) and mobile phones (MPs) before seeking medical attention, making it possible to investigate the influenza prevalence by monitoring changes in frequencies of uses of particular search terms. In this study, we collected the data from H7N9 epidemics and H7N9-related Baidu Search Index (BSI) in China between September 2013 and June 2017. And then, we showed a strong correlation between the numbers of Influenza A (H7N9) cases and H7N9-related BSI in Guangdong province and Shanghai municipality (p<0.001). Furthermore, we reconstructed an improved dynamic forecasting method for outbreaks of H7N9 influenza using Autoregressive integrated moving average (ARIMA) models to predict future patterns of H7N9 transmission and the online search data acted as an external regressor with the historical H7N9 epidemic data in the forecasting model to improve the quality of predictions. Our results suggest that data from the Baidu search engine, combed with data from a traditional disease surveillance system, may be considered for early detection of H7N9 influenza outbreaks in mainland China.

scholarly journals An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy

2020 ◽  
Author(s):  
Gaetano Perone
Keyword(s):  
Inflection Point ◽  
Social Impact ◽  
Moving Average ◽  
Arima Model ◽  
Epidemiological Data ◽  
List Type ◽  
Arima Models ◽  
Final Size ◽  
Autoregressive Integrated Moving Average ◽  
The World

AbstractCoronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to April 4, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemic’s inflection point and final size.Highlights❖ARIMA models allow in an easy way to investigate COVID-2019 trends, which are nowadays of huge economic and social impact.❖These data may be used by the health authority to continuously monitor the epidemic and to better allocate the available resources.❖The results suggest that the epidemic spread inflection point, in term of cumulative cases, will be reached at the end of May.❖Further useful and more precise forecasting may be provided by updating these data or applying the model to other regions and countries.

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