scholarly journals Analysis of the demand for gastronomic tourism in Andalusia (Spain)

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246377
Author(s):  
Mª Genoveva Dancausa Millán ◽  
Mª Genoveva Millán Vázquez de la Torre ◽  
Ricardo Hernández Rojas
Keyword(s):  
Olive Oil ◽  
Raw Materials ◽  
Moving Average ◽  
Food Products ◽  
Southern Spain ◽  
Autoregressive Integrated Moving Average ◽  
Wine Tourism ◽  
Protected Designations Of Origin ◽  
Sarima Models

In recent years, gastronomy has become a fundamental motivation to travel. Learning how to prepare gastronomic dishes and about the raw materials that compose them has attracted increasing numbers of tourists. In Andalusia (region of southern Spain), there are many quality products endorsed by Protected Designations of Origin, around which gastronomic routes have been created, some visited often (e.g., wine) and others remaining unknown (e.g., ham and oil). This study analyses the profile of gastronomic tourists in Andalusia to understand their motivations and estimates the demand for gastronomic tourism using seasonal autoregressive integrated moving average (SARIMA) models. The results obtained indicate that the gastronomic tourist in Andalusia is very satisfied with the places he/she visits and the gastronomy he/she savours. However, the demand for this tourist sector is very low and heterogeneous; while wine tourism is well established, tourism focusing on certain products, such as olive oil or ham, is practically non-existent. To obtain a homogeneous demand, synergies or pairings should be created between food products, e.g., wine-ham, oil-ham, etc., to attract a greater number of tourists and distinguish Andalusia as a gastronomic holiday destination.

scholarly journals Forecasting wholesale price of coarse rice in Bangladesh: A seasonal autoregressive integrated moving average approach

2014 ◽  
Vol 11 (2) ◽  
pp. 271-276
Author(s):  
MF Hassan ◽  
MA Islam ◽  
MF Imam ◽  
SM Sayem
Keyword(s):  
Moving Average ◽  
Wholesale Price ◽  
Information Criteria ◽  
Suitable Model ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Lag Times ◽  
Predicted Values ◽  
Absolute Percent Error ◽  
Sarima Models

This article attempts to develop the model and to forecast the wholesale price of coarse rice in Bangladesh. Seasonal Autoregressive Integrated Moving Average (SARIMA) models have been developed on the monthly data collected from July 1975 to December 2011and validated using the data from December 2010 to December 2011. The results showed that the predicted values were consistent with the upturns and downturns of the observed series. The model with non seasonal autoregressive 1, difference 1 and moving average 1 and seasonal difference 1 and moving average 1 that is SARIMA (1,1,1)(0,1,1)12 model has been found as the most suitable model with least Root Mean Square Error (RMSE) of 61.657, Normalised Bayesian Information Criteria (BIC) of 8.300 and Mean Absolute Percent Error (MAPE) of 3.906. The model was further validated by Ljung-Box test (Q18=17.394 and p>.20) with no significant autocorrelation between residuals at different lag times. Finally, a forecast for the period January 2012 to December 2013 was made. DOI: http://dx.doi.org/10.3329/jbau.v11i2.19925 J. Bangladesh Agril. Univ. 11(2): 271-276, 2013

scholarly journals Dark Tourism in Southern Spain (Córdoba): An Analysis of the Demand

2021 ◽  
Vol 18 (5) ◽  
pp. 2740
Author(s):  
María Genoveva Dancausa Millán ◽  
María Genoveva Millán Vázquez de la Torre ◽  
Ricardo Hernández Rojas
Keyword(s):  
Moving Average ◽  
Southern Spain ◽  
Arima Models ◽  
Dark Tourism ◽  
Autoregressive Integrated Moving Average ◽  
The People ◽  
New Type ◽  
Cultural Tourist

In recent decades, there has been a change in tourists’ tastes; they want to experience something novel. To satisfy this demand, a new type of tourism, known as “dark tourism”, has arisen; it has various modalities, among which cemetery tourism and ghost tourism stand out, in addition to very different motivations from those of the cultural tourist. In this type of tourism, cemeteries are not visited to appreciate their architecture or heritage but to explore a morbid curiosity about the people buried there; ghost tourism or paranormal tourism seizes on the desire to know the events that occurred there and tends to have macabre content. This study analyzes dark tourism in the province of Córdoba in southern Spain with the aim of knowing the profile of the tourist and his motivation. This study additionally will forecast the demand for this type of tourism, using autoregressive integrated moving average (ARIMA) models, which allow us to know this market’s evolution and whether any promotional action should be carried out to promote it.

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

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%.

scholarly journals Econometric Models for Forecasting Remittances of Bangladesh

2017 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Tamanna Islam ◽  
Ashfaque A. Mohib ◽  
Shahnaz Zarin Haque
Keyword(s):  
Foreign Exchange ◽  
Migrant Workers ◽  
Industrial Development ◽  
Raw Materials ◽  
Moving Average ◽  
Critical Role ◽  
Foreign Exchange Earning ◽  
Autoregressive Integrated Moving Average ◽  
Future Situation ◽  
Autoregressive Conditional Heteroscedasticity

At present, the remittance of Bangladesh (RB) is the largest source of foreign exchange earning of the country. The RB plays a critical role in alleviating the foreign-exchange constraint and supporting the balance of payments, enabling imports of capital goods and raw materials for industrial development. Remittance from overseas migrant workers certainly increases the income disparity between classes of the rural society. Therefore forecasting plays an important role to know the future situation of economic condition. This paper employed the prospective data on RB to derive a unique and suitable forecasting model. The data were collected from Bangladesh Bank (BB) during January, 1998 to December, 2003. The Autoregressive Integrated Moving Average (ARIMA) and the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models were used to find out the best one. The findings indicated that the ARIMA (0,1,1) (0,2,1)12 and the GARCH (2,1) models were appropriate for our data and the GARCH (2,1) model appeared to be the best one between these.

scholarly journals Associations Between Canada's Cannabis Legalization and Emergency Department Presentations for Transient Cannabis-Induced Psychosis and Schizophrenia Conditions: Ontario and Alberta, 2015–2019

2022 ◽  
pp. 070674372110706
Author(s):  
Russell C. Callaghan ◽  
Marcos Sanches ◽  
Robin M. Murray ◽  
Sarah Konefal ◽  
Bridget Maloney-Hall ◽  
...  
Keyword(s):  
Emergency Department ◽  
Moving Average ◽  
Step Function ◽  
Autoregressive Integrated Moving Average ◽  
Study Results ◽  
Icd 10 ◽  
Significant Step ◽  
October 17 ◽  
Comparison Series ◽  
Sarima Models

Objective Cannabis legalization in many jurisdictions worldwide has raised concerns that such legislation might increase the burden of transient and persistent psychotic illnesses in society. Our study aimed to address this issue. Methods Drawing upon emergency department (ED) presentations aggregated across Alberta and Ontario, Canada records (April 1, 2015–December 31, 2019), we employed Seasonal Autoregressive Integrated Moving Average (SARIMA) models to assess associations between Canada's cannabis legalization (via the Cannabis Act implemented on October 17, 2018) and weekly ED presentation counts of the following ICD-10-CA-defined target series of cannabis-induced psychosis (F12.5; n = 5832) and schizophrenia and related conditions (“schizophrenia”; F20-F29; n = 211,661), as well as two comparison series of amphetamine-induced psychosis (F15.5; n = 10,829) and alcohol-induced psychosis (F10.5; n = 1,884). Results ED presentations for cannabis-induced psychosis doubled between April 2015 and December 2019. However, across all four SARIMA models, there was no evidence of significant step-function effects associated with cannabis legalization on post-legalization weekly ED counts of: (1) cannabis-induced psychosis [0.34 (95% CI −4.1; 4.8; P = 0.88)]; (2) schizophrenia [24.34 (95% CI −18.3; 67.0; P = 0.26)]; (3) alcohol-induced psychosis [0.61 (95% CI −0.6; 1.8; P = 0.31); or (4) amphetamine-induced psychosis [1.93 (95% CI −2.8; 6.7; P = 0.43)]. Conclusion Implementation of Canada's cannabis legalization framework was not associated with evidence of significant changes in cannabis-induced psychosis or schizophrenia ED presentations. Given the potentially idiosyncratic rollout of Canada's cannabis legalization, further research will be required to establish whether study results generalize to other settings.

scholarly journals Forecasting Sports Popularity: Application of Time Series Analysis

2017 ◽  
Vol 6 (2) ◽  
pp. 75-82 ◽  
Author(s):  
Ryan Miller ◽  
Harrison Schwarz ◽  
Ismael S. Talke
Keyword(s):  
Performance Measures ◽  
Historical Data ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Search Volume ◽  
Forecasting Performance ◽  
Search Data ◽  
Sports Leagues ◽  
The Times ◽  
Sarima Models

Abstract Popularity trends of the NFL and NBA are fun and interesting for casual fans while also of critical importance for advertisers and businesses with an interest in the sports leagues. Sports leagues have clear and distinct seasons and these have a major impact on when each league is most popular. To measure the popularity of each league, we used search data from Google Trends that gives real-time and historical data on the relative popularity of search words. By using search volume to measure popularity, the times of year, a sport is popular relative to its season can be explained. It is also possible to forecast how sport leagues are trending relative to each other. We compared and discussed three different univariate models both theoretically and empirically: the trend plus seasonality regression, Holt- Winters Multiplicative (HWMM), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to determine the popularity trends. For each league, the six forecasting performance measures used in this study indicated HWMM gave the most accurate predictions.

scholarly journals Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

2021 ◽  
Vol 2 (6) ◽  
pp. 50-63
Author(s):  
Teddy Mutugi Wanjuki ◽  
Adolphus Wagala ◽  
Dennis K. Muriithi
Keyword(s):  
Monetary Policy ◽  
Fiscal Policy ◽  
Price Index ◽  
Moving Average ◽  
Estimation Method ◽  
Series Data ◽  
Policy Objective ◽  
Autoregressive Integrated Moving Average ◽  
Food And Beverages ◽  
Sarima Models

Price stability is the primary monetary policy objective in any economy since it protects the interests of both consumers and producers. As a result, forecasting is a common practice and a vital aspect of monetary policymaking. Future predictions guide monetary and fiscal policy tools that that be used to stabilize commodity prices. As a result, developing an accurate and precise forecasting model is critical. The current study fitted and forecasted the food and beverages price index (FBPI) in Kenya using seasonal autoregressive integrated moving average (SARIMA) models. Unlike other ARIMA models like the autoregressive (AR), Moving Average (MA), and non-seasonal ARMA models, the SARIMA model accounts for the seasonal component in a given time series data better forecasts. The study relied on secondary data obtained from the KNBS website on monthly food and beverage price index in Kenya from January 1991 to February 2020. R-statistical software was used to analyze the data. The parameter estimation was done using the Maximum Likelihood Estimation method. Competing SARIMA models were compared using the Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE),.and Mean Absolute Percentage Error (MAPE). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The forecasting ability evaluation statistics MAE = 2.00%, MAPE = 1.62% and MASE = 0.87%. The 24-step ahead forecasts showed that the FPBI is unstable with an overall increasing trend. Therefore, the monetary policy committee ought to control inflation through monetary or fiscal policy, strengthening food security and trade liberalization.

scholarly journals Forecasting the COVID-19 Increment Rate in DKI Jakarta Using Non-Robust STL Decomposition and SARIMA Model

2021 ◽  
Vol 7 (1) ◽  
pp. 21-30
Author(s):  
Rosmelina Deliani Satrisna ◽  
Aniq A. Rohmawati ◽  
Siti Sa’adah
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Sarima Model ◽  
Forecasting Accuracy ◽  
Modeling And Analysis ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
Daily Number ◽  
Increment Rate ◽  
Sarima Models

The Corona virus known as COVID-19 was first present in Wuhan, China at this time has troubled many countries and its spread is very fast and wide. Data on daily confirmed COVID-19 cases were collected from the DKI Jakarta province between early May 2020 and late January 2021. The daily increase in confirmed COVID-19 cases has a percentage of the value of increase in total cases. In this study, modeling and analysis of forecasting the increment rate in daily number of new cases COVID-19 DKI Jakarta was carried out using the Seasonal-Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. STL Decomposition is a form of algorithm developed to help decompose a Time Series, and techniques considering seasonal and non-stationary observation. The results of the best forecasting accuracy are proven by STL-ARIMA, there are MAPE and MSE which only have an error value of 0.15. This proposed approach can be used for consideration for the DKI Jakarta government in making policies for handling COVID-19, as well as for the public to adhere to health protocols.

A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data

2015 ◽  
Vol 26 (1) ◽  
pp. 125-137 ◽  
Author(s):  
Edmore Ranganai ◽  
Mphiliseni B Nzuza
Keyword(s):  
Stochastic Models ◽  
Moving Average ◽  
Ground Level ◽  
Atmospheric Conditions ◽  
Deterministic Models ◽  
Autoregressive Integrated Moving Average ◽  
Modelling Procedure ◽  
Irradiance Data ◽  
Short Memory ◽  
Sarima Models

Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such data with some degree of success. This success is attributable to its ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. However, irradiance data recorded at the earth’s surface is rarely entirely stochastic but a mixture of both deterministic and stochastic components. One plausible modelling procedure is to couple sinusoidal predictors at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle, with SARIMA models capturing the stochastic components. We construct such models which we term, harmonically coupled SARIMA (HCSARIMA) models and use them to empirically model the global horizontal irradiance (GHI) recorded at the earth’s surface. Comparison of the two classes of models shows that HCSARIMA models generally out-compete SARIMA models in the forecasting arena.

scholarly journals La importancia de las denominaciones de origen protegidas como indicadores de calidad para el comportamiento del consumidor. El caso del aceite de oliva del Bajo Aragón

2011 ◽  
Vol 7 (14) ◽  
pp. 3 ◽  
Author(s):  
Joel Espejel ◽  
Carmina Fandos ◽  
Carlos Flavián
Keyword(s):  
Olive Oil ◽  
Raw Materials ◽  
Food Product ◽  
Food Products ◽  
Perceived Quality ◽  
Know How

In this paper we analyze the influence of satisfaction towards a food product with Protected Denomination of Origin (PDO) on loyalty and consumers’ buying intention. The results show the importance that the consumers allowance towards the links as the origin, territory, raw materials, know how, and the strict controls by the PDO Regulatory Council. All these aspects, concern in the determination of the perceived quality of the above mentioned PDO food products. In fact, these findings suppose a major feelings satisfaction and loyalty as well as of a major predisposition to continue buying the olive oil from Bajo Aragon (Spain).

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