scholarly journals Analysis Prediction of Prambanan Temple Visitors with Fuzzy Time Series Chen Model and Seasonal Auto Regressive Integrated Moving Average (SARIMA) Model

2020 ◽  
Vol 3 ◽  
pp. 359-363
Author(s):  
Rohmah Artika ◽  
Adi Setiawan ◽  
Glagah Eskacakra Setyowisnu ◽  
Siti Uminasiah ◽  
Prihantini
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Fuzzy Time Series ◽  
Sarima Model ◽  
Tourist Attractions ◽  
The Future ◽  
Auto Regressive ◽  
Analysis Prediction ◽  
Seasonal Data

The number of visitors in tourist attractions are almost always changes each time, even for tourist attractions that are already well-known among local and foreign people, usually will tend to increase at certain times, as in the Prambanan Temple. Based on data from TWC (Taman Wisata Candi) unit office, the number of visitors of Prambanan Temple during holidays at the end of 2018 increased by 8% from the previous year. Because of its increase, the manager of tourist attractions must always try to provide the best service. Therefore, the manager of Prambanan Temple needs to know the prediction of the number of visitors in the future so that they can prepare services and innovations to increase its attractiveness. The data of Prambanan Temple visitors number is seasonal, so the visitors number prediction at Prambanan Temple will be determined using the method for seasonal data. This research tries to compare the two methods, namely Fuzzy Time Series Chen Model and Seasonal Auto Regressive Integrated Moving Average (SARIMA) Model. The results of these methods are the visitors number prediction with different errors, so it can be seen which method is better between the two.

scholarly journals Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 240 ◽  
Author(s):  
Mohammed Alsharif ◽  
Mohammad Younes ◽  
Jeong Kim
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
South Korea ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Sarima Model ◽  
Radiation Data ◽  
Partial Autocorrelation Function ◽  
Auto Regressive

Forecasting solar radiation has recently become the focus of numerous researchers due to the growing interest in green energy. This study aims to develop a seasonal auto-regressive integrated moving average (SARIMA) model to predict the daily and monthly solar radiation in Seoul, South Korea based on the hourly solar radiation data obtained from the Korean Meteorological Administration over 37 years (1981–2017). The goodness of fit of the model was tested against standardized residuals, the autocorrelation function, and the partial autocorrelation function for residuals. Then, model performance was compared with Monte Carlo simulations by using root mean square errors and coefficient of determination (R2) for evaluation. In addition, forecasting was conducted by using the best models with historical data on average monthly and daily solar radiation. The contributions of this study can be summarized as follows: (i) a time series SARIMA model is implemented to forecast the daily and monthly solar radiation of Seoul, South Korea in consideration of the accuracy, suitability, adequacy, and timeliness of the collected data; (ii) the reliability, accuracy, suitability, and performance of the model are investigated relative to those of established tests, standardized residual, autocorrelation function (ACF), and partial autocorrelation function (PACF), and the results are compared with those forecasted by the Monte Carlo method; and (iii) the trend of monthly solar radiation in Seoul for the coming years is analyzed and compared on the basis of the solar radiation data obtained from KMS over 37 years. The results indicate that (1,1,2) the ARIMA model can be used to represent daily solar radiation, while the seasonal ARIMA (4,1,1) of 12 lags for both auto-regressive and moving average parts can be used to represent monthly solar radiation. According to the findings, the expected average monthly solar radiation ranges from 176 to 377 Wh/m2.

scholarly journals Seasonal Autoregressive Integrated Moving Average (SARIMA) Model for the Analysis of Frequency of Monthly Rainfall in Osun State, Nigeria

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Samuel Olorunfemi Adams ◽  
Bello Mustapha ◽  
Auta Irinew Alumbugu
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Original Data ◽  
Monthly Rainfall ◽  
Time Series Modeling ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Augmented Dickey Fuller ◽  
Auto Regressive ◽  
Best Fit

The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is proposed for Osun State monthly rainfall data and the analysis was based on probability time series modeling approach. The Plot of the original data shows that the time series is stationary and the Augmented Dickey-Fuller test did not suggest otherwise. The graph further displays evidence of seasonality and it was removed by seasonal differencing. The plots of the ACF and PACF show spikes at seasonal lags respectively, suggesting SARIMA (1, 0, 1) (2, 1, 1). Though the diagnostic check on the model favoured the fitted model, the Auto Regressive parameter was found to be statistically insignificant and this led to a reduced SARIMA (1, 0, 1) (1, 1, 1)  model that best fit the data and was used to make forecast.

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.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

Modeling Time Series for Prediction of Thalassemia in Nineveh Governorate

2021 ◽  
Vol 2 (3) ◽  
pp. 120-131
Author(s):  
Shaymaa Riyadh Thanoon
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Monthly Data ◽  
Number Of Patients ◽  
Auto Regressive

The aim of this research is to analyze the time series of Thalassemia cancer cases by making assumptions on the number of cases to formulate the problem to find the best model for predicting the number of patients in Nineveh governorate using (Box and Jenkins) method of analysis based on the monthly data provided by Al Salam Hospital in Nineveh for the period (2014-2018). The results of the analysis showed that the appropriate model of analysis is the Auto-Regressive Integrated Moving Average (ARIMA) (2,1,0) and based on this model the number of people with this disease was predicted for the next two years where the results showed values ​​consistent with the original values which indicates the good quality of the model.

scholarly journals Influence of seasonality, salinity and temperature on catch trend of Penaeus indicus H. Milne-Edwards, 1837 in a coastal lagoon, India

2019 ◽  
Vol 66 (1) ◽  
Author(s):  
R.K. Raman ◽  
V.R. Suresh ◽  
S.K. Mohanty ◽  
K.S. Bhatta ◽  
S.K. Karna ◽  
...  
Keyword(s):  
Time Series ◽  
Coastal Lagoons ◽  
Moving Average ◽  
Positive Influence ◽  
Chilika Lagoon ◽  
Explanatory Variables ◽  
Structural Time Series ◽  
Water Temperature Data ◽  
Penaeus Indicus ◽  
Auto Regressive

The catch pattern of P. indicus in coastal lagoons is influenced by seasonal changes in physicochemical parameters of the lagoon ecosystem. In this study the effects of seasonality, salinity and water emperature of lagoon on P. indicus catch were analysed using Structural Time Series Model (STSM) and ARIMAX (Auto Regressive Integrated Moving Average with explanatory variables) modeling approach using monthly time series catch, salinity and water temperature data of the Chilika Lagoon (a Ramsar site) in India for the period from 2001 to 2015. Results showed a significant (p<0.05) increasing stochastic upward trend and two seasonal cycles for P. indicus catch in the lagoon. Salinity was found to have significant positive influence (p<0.05) and temperature to have insignificant positive influence on P. indicus catch in the lagoon.

scholarly journals Time-Series Statistical Model for Forecasting Revenue and Risk Management

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Mihir Kelkar ◽  
Cosmin Borsa ◽  
Lina Kim
Keyword(s):  
Time Series ◽  
Low Cost ◽  
Moving Average ◽  
Business Strategies ◽  
Annual Reports ◽  
Fiscal Year ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Low Cost Carrier ◽  
Southwest Airlines

Following a Low-Cost Carrier (LCC) model, Southwest Airlines has consistently demonstrated growing annual revenues up until the start of the COVID-19 pandemic. Southwest’s quarterly revenue shows that there exists a strong seasonal component with the revenue in the first quarter of the fiscal year (September) significantly higher than other quarters. Using the quarterly revenue data we constructed a time-series model: a seasonal autoregressive integrated moving average (SARIMA) model to forecast Southwest’s revenue over 2020. We then performed a cost and solvency risk analysis using the company’s financial results from its annual reports to analyze Southwest’s financial performance due to COVID-19, and proposed business strategies to keep Southwest financially stable.

scholarly journals Forecasting and Modelling of Solar Radiation for Photovoltaic (PV) Systems

2021 ◽  
Author(s):  
Ines Sansa ◽  
Najiba Mrabet Bellaaj
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Cloud Cover ◽  
Moving Average ◽  
Arma Model ◽  
Weather Conditions ◽  
Pv Systems ◽  
Auto Regressive ◽  
Micro Grid ◽  
One Year

Solar radiation is characterized by its fluctuation because it depends to different factors such as the day hour, the speed wind, the cloud cover and some other weather conditions. Certainly, this fluctuation can affect the PV power production and then its integration on the electrical micro grid. An accurate forecasting of solar radiation is so important to avoid these problems. In this chapter, the solar radiation is treated as time series and it is predicted using the Auto Regressive and Moving Average (ARMA) model. Based on the solar radiation forecasting results, the photovoltaic (PV) power is then forecasted. The choice of ARMA model has been carried out in order to exploit its own strength. This model is characterized by its flexibility and its ability to extract the useful statistical properties, for time series predictions, it is among the most used models. In this work, ARMA model is used to forecast the solar radiation one year in advance considering the weekly radiation averages. Simulation results have proven the effectiveness of ARMA model to forecast the small solar radiation fluctuations.

Forecasting e-waste recovery scale driven by seasonal data characteristics: A decomposition-ensemble approach

2021 ◽  
pp. 0734242X2110614
Author(s):  
AKM Mohsin ◽  
Lei Hongzhen ◽  
Mohammed Masum Iqbal ◽  
Zahir Rayhan Salim ◽  
Alamgir Hossain ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Production Capacity ◽  
Waste Recycling ◽  
Support Vector ◽  
Waste Recovery ◽  
Decomposition Procedure ◽  
Moving Average Model ◽  
The Government ◽  
Seasonal Data

Forecasting the scale of e-waste recycling is the basis for the government to formulate the development plan of circular economy and relevant subsidy policies and enterprises to evaluate resource recovery and optimise production capacity. In this article, the CH-X12 /STL-X framework for e-waste recycling scale prediction is proposed based on the idea of ‘decomposition-integration’, considering that the seasonal data characteristics of quarterly e-waste recycling scale data may lead to large forecasting errors and inconsistent forecasting results of a traditional single model. First, the seasonal data characteristics of the time series of e-waste recovery scale are identified based on Canova–Hansen (CH) test, and then the time series suitable for seasonal decomposition is extracted with X12 or seasonal-trend decomposition procedure based on loess (STL) model for seasonal components. Then, the Holt–Winters model was used to predict the seasonal component, and the support vector regression (SVR) model was used to predict the other components. Finally, the linear sum of the prediction results of each component is used to obtain the final prediction result. The empirical results show that the proposed CH-X12/STL-X forecasting framework can better meet the modelling requirements for time-series forecasting driven by different seasonal data characteristics and has better and more stable forecasting performance than traditional single models (Holt–Winters model, seasonal autoregressive integrated moving average model and SVR model).

Predicting Daily Confirmed COVID-19 Cases in India

2021 ◽  
pp. 34-51
Author(s):  
Sudip Singh
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Family Welfare ◽  
Mean Square ◽  
Univariate Time Series ◽  
Partial Autocorrelation Function ◽  
Auto Regressive

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

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