Developing Monthly Hydrometeorological Timeseries Forecasts To Reservoir Operation in a Transboundary River Catchment

Abstract Forecasting of hydrometeorological timeseries data play vital role in flood forecasting and predicting the future water availability for various uses such as irrigation, hydropower generation, industrial, domestic, etc. Therefore, present study aims to forecast the hydrometeorological timeseries data, i.e., river inflows, precipitation, and evaporation for the improved reservoir operation of a transboundary Mangla catchment by using ARIMA (auto-regressive integrated moving average) model. Prior to applying the ARIMA model, stationarity of hydrometeorological timeseries data was checked. Moreover, ACF and PACF of timeseries were determined to determine the “p” and “q” terms of the ARIMA model. The best fitted structure of ARIMA model was used by evaluating the R2, MAE and RMSE to forecast the hydrometeorological timeseries. The seasonal ARIMA structure of (1,0,0)(2,1,2)12 was found best fitted for the inflow timeseries whereas ARIMA structures of (14,1,15) and (9,1,19) were considered for forecasting the precipitation and evaporation timeseries, respectively. An average water shortage of 14% was detected by using these forecasted hydrometeorological timeseries in the reservoir operation during the period of 2016–2030. It was also observed that inflows into Mangla reservoir have seasonal effect more prominent compared to evaporation and precipitation. However, variations in the precipitation timeseries were found less smooth than the inflows timeseries. It is believed that the results of this study may support reservoir operators and managers for developing efficient real-rime reservoir operation policies and strategies based on the variations in the future water availability.

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A statistical study of COVID-19 pandemic in Egypt

Demonstratio Mathematica ◽

10.1515/dema-2021-0028 ◽

2021 ◽

Vol 54 (1) ◽

pp. 233-244

Author(s):

Taha Radwan

Keyword(s):

Linear Relationship ◽

Statistical Study ◽

Moving Average ◽

Arima Model ◽

Decision Makers ◽

Accurate Data ◽

Autoregressive Integrated Moving Average ◽

The World ◽

The Future

Abstract The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA) model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.

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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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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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Forecasting Techniques for Sales Prediction

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e5973.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 4924-4927

Keyword(s):

Moving Average ◽

Arima Model ◽

Fuel Price ◽

Common People ◽

Forecasting Models ◽

Sales Prediction ◽

The Future ◽

Feed Forward Neural Networks ◽

Past Data ◽

Predicted Values

All of us are very curious about future, very excited to know what will happen in the next moment. Similarly, retailers are also curious about the future of their business, its progress and their future sales. Walmart is the world’s biggest retailer and also has a vast grocery chain over the world. It was initially established in America 1962. In 2019, it has more than 11,000 stores in 28 countries but the sales differ from place to place. Many sales strategies, discount rates will be introduced for the improvement of sales. Retailers always try to attract the common people to visit their store. They always focus on improving the future sales. Using some Machine learning forecasting models, we can estimate the future sales based on the past data. Our aim is to apply time series forecasting models to retail sales data, which contains weekly sales of 45 Walmart stores across United States from 2010 to 2012. There are other factors which effects the analysis of weekly sales - markdown, consumer per index, Is Holiday (boolean value returns whether it is holiday or not), size of the store, unemployment, store type, fuel price and temperature. The forecasting models applied for the data are Autoregressive Integrated Moving Average (ARIMA) model and Feed Forward Neural Networks (FFNN). The dataset will be divided into training and testing datasets. The predicted values will be checked with the test data and accuracy will be calculated. Based on the accuracy we conclude which of the two models will better for the sales prediction.

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Forecasting of Paddy Grain and Rice's Price: An ARIMA (Autoregressive Integrated Moving Average) Model Application

SOCA: Jurnal Sosial Ekonomi Pertanian ◽

10.24843/soca.2020.v14.i02.p04 ◽

2020 ◽

Vol 14 (2) ◽

pp. 224

Author(s):

Fitri Ramadhani ◽

Ketut Sukiyono ◽

Melli Suryanty

Keyword(s):

Moving Average ◽

Arima Model ◽

Future Price ◽

International Level ◽

Price Prediction ◽

Rice Price ◽

Stationary Test ◽

Moving Average Model ◽

Actual Price

A decision making for a long-term paddy grain and rice price guidelines need a future price prediction and a forecasting model that made based on time progression. The most popular model used is ARIMA. The common problem in forecasting the paddy grain and rice in Indonesia using this model was choosing the best model which fit all type of forecasting. This study aimed to determine the most appropriate ARIMA Model and forecast paddy grain and rice’s price on the farmer level, wholesale level, and international level. The prediction began after the stationary test and the best model selection conducted. The ARIMA model used was chosen by the lowest AIC and SC accuracy value. ARIMA Model used in this study were grain price on the farmer level (1,1,2), grain price on the milling level (1,1,2), rice price on the wholesale level (1,1,3), and rice price on the international level (3,1,7). The rice price prediction in the next sixth months on the farmer level was IDR 5,905.15/kg and the actual price was IDR 5,524.89/kg, on the milling level was IDR 6,014.35/kg and the actual price was IDR 5,641/kg, on the wholesale level was IDR 12,163.92/kg and the actual price IDR 12,115/kg, while the on the international level was US$ 462,065/Ton and the actual price was US$ 408/Ton. This study concluded that the price list at a different level of the market was requiring a different model of ARIMA.

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Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia

Practice and Theory in Systems of Education ◽

10.1515/ptse-2017-0005 ◽

2017 ◽

Vol 12 (1) ◽

pp. 43-50

Author(s):

Umi Mahmudah

Keyword(s):

Time Series Data ◽

Moving Average ◽

Arima Model ◽

Series Data ◽

Average Model ◽

Autoregressive Integrated Moving Average ◽

Moving Average Model ◽

Forecasting Analysis ◽

Graduate Unemployment ◽

Point Forecast

AbstractNowadays it is getting harder for higher education graduates in finding a decent job. This study aims to predict the graduate unemployment in Indonesia by using autoregressive integrated moving average (ARIMA) model. A time series data of the graduate unemployment from 2005 to 2016 is analyzed. The results suggest that ARIMA (1,2,0) is the best model for forecasting analysis, where there is a tendency of increasing number for the next ten periods. Furthermore, the average of point forecast for the next 10 periods is about 1,266,179 while its minimum value is 1,012,861 the maximum values is 1,523,156. Overall, ARIMA (1,2,0) provides an adequate forecasting model so that there is no potential for improvement.

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An Application of ARIMA Model to Forecast the Dynamics of COVID-19 Epidemic in India

Global Business Review ◽

10.1177/0972150920988653 ◽

2021 ◽

pp. 097215092098865

Author(s):

Rupinder Katoch ◽

Arpit Sidhu

Keyword(s):

Temporal Dynamics ◽

Time Window ◽

Moving Average ◽

Arima Model ◽

National Level ◽

Epidemiological Data ◽

Final Size ◽

The Past ◽

The Future ◽

Near Future

The swiftly growing and overwhelming epidemic in India has intensified the question: What will the trend and magnitude of impact of the novel coronavirus disease 2019 (COVID-19) be in India in the near future? To answer the present question, the study requires ample historical data to make an accurate forecast of the blowout of expected confirmed cases. All at once, no prediction can be certain as the past seldom reiterates itself in the future likewise. Besides, forecasts are influenced by a number of factors like reliability of the data and psychological factors like perception and reaction of the people to the hazards arising from the epidemic. The present study presents a simple but powerful and objective, that is, autoregressive integrated moving average (ARIMA) approach, to analyse the temporal dynamics of the COVID-19 outbreak in India in the time window 30 January 2020 to 16 September 2020 and to predict the final size and trend of the epidemic over the period after 16 September 2020 with Indian epidemiological data at national and state levels. With the assumption that the data that have been used are reliable and that the future will continue to track the same outline as in the past, underlying forecasts based on ARIMA model suggest an unending increase in the number of confirmed COVID-19 cases in India in the near future. The present article suggests varying epidemic’s inflection point and final size for underlying states and for the mainland, India. The final size at national level is expected to reach 25,669,294 in the next 230 days, with infection point that can be expected to be projected only on 23 April 2021. The study has enormous potential to plan and make decisions to control the further spread of epidemic in India and provides objective forecasts for the confirmed cases of COVID-19 in the coming days corresponding to the respective COVID periods of the underlying regions.

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Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling

Geofluids ◽

10.1155/2020/8851475 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Yi-Hui Pang ◽

Hong-Bo Wang ◽

Jian-Jian Zhao ◽

De-Yong Shang

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

Arima Model ◽

Sliding Window ◽

Series Data ◽

Average Model ◽

Hydraulic Support ◽

Autoregressive Integrated Moving Average ◽

Moving Average Model

Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.

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Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

Journal of Information Technology and Computer Science ◽

10.25126/jitecs.202051117 ◽

2020 ◽

Vol 5 (1) ◽

pp. 13

Author(s):

Dwi Asa Verano ◽

Husnawati Husnawati ◽

Ermatita Ermatita

Keyword(s):

Raw Materials ◽

Moving Average ◽

Arima Model ◽

Information Criterion ◽

Raw Material ◽

Digital Printing ◽

Printing Industry ◽

Autoregressive Integrated Moving Average ◽

Moving Average Model

The technology used in the printing industry is currently growing rapidly. Generally, the digital printing industry uses raw materials in the form of paper production. The use of paper material with large volumes is clear badly in need of purchasing large quantities of paper stock as well. The purchase of paper stocks with a constant amount at the beginning of each month for various types of paper causes a buildup or lack of material stock standard on certain types of paper. During this time the purchase and ordering of raw materials only based on the estimates or predictions of the owner. In this paper proposed forecasting will be carried out in the digital printing industry by applying the ARIMA model for each type of raw material paper with the Palembang F18 digital printing case study. The ARIMA modeling applied will produce different parameters for each materials paper type so as to produce forecasting with the Akaike Information Criterion (AIC) value averages 13.0294%.

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Time Series Modelling of The Volume of Overland Timber Export in The Bolgatanga Municipality

Journal of Scientific Research and Reports ◽

10.9734/jsrr/2020/v26i430253 ◽

2020 ◽

pp. 124-138

Author(s):

Henry M. Kpamma ◽

Silverius K. Bruku ◽

John A. Awaab

Keyword(s):

Time Series ◽

Moving Average ◽

Preliminary Investigation ◽

Arima Model ◽

Estimation Method ◽

Long Run ◽

Time Series Modelling ◽

The Future ◽

Equilibrium Relationships ◽

The Right

Aims/ Objectives: This research was carried out with the intention of using time series to model the volume of overland timber exported within Bolgatanga municipalityPlace and Duration of Study: Study of the time series was based on a historical data of the volume of timber exported for twenty consecutive years, from 1999 to 2019 within Bolgatanga municipality.Methodology: The three-stage iterative modeling approach for Box Jenkins was used to match an ARIMA model and to forecast both the amount of timber export and the confiscated lumber. ARIMA method incorporates a cycle of autoregressive and a moving average. The three-stage iterative modeling technique of Box Jenkins which were used are model recognition, parameter estimation and/or diagnostic checks were also made. Results: From the preliminary investigation, the study showed that the amount of timber exported in municipality is skewed to the right, suggesting that much of the amount of timber exported is below the average. This, together with the high volatility in the volume of timber exported, indicates that the amount of timber exported within the municipalities during the twenty-year period was low. The plots from the trends also showed robust variations in the volume of timber exported indicating that timber exporters do not have better grips with the concepts and applications of export technology, hence the erratic nature of the volume of timber exported over the period. The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported.The analysis further indicated that there will be a further decrease in the amount of timber export from the five years projection into the future. Over the last two decades the Bayesian approach to VAR has gained ground. For a future report, this estimation method will be followed to examine the ”long-run equilibrium relationships” between timber export volumes and climate change.Conclusion: The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported. There will be a further decrease in the amount of timber export from the five years projection into the future.

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