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.

Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry

2016 ◽  
Vol 7 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Nari Sivanandam Arunraj ◽  
Diane Ahrens ◽  
Michael Fernandes
Keyword(s):  
Influencing Factors ◽  
Performance Measures ◽  
Moving Average ◽  
Food Product ◽  
Retail Store ◽  
Food Supply Chain ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Fresh Food ◽  
External Variables

During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model.

scholarly journals The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

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.

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 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 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 Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

Forecasting ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 211-229
Author(s):  
Ulrich Gunter ◽  
Irem Önder ◽  
Egon Smeral
Keyword(s):  
Moving Average ◽  
Tourism Demand ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Forecast Combinations ◽  
The Stability ◽  
Forecasting Errors ◽  
Macroeconomic Environment

This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results.

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 Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development

2019 ◽  
Vol 11 (9) ◽  
pp. 2482
Author(s):  
Linlin Zhao ◽  
Jasper Mbachu ◽  
Zhansheng Liu
Keyword(s):  
New Zealand ◽  
Regression Model ◽  
House Prices ◽  
Moving Average ◽  
Arima Model ◽  
Housing Prices ◽  
Housing Sector ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Performance ◽  
Sample Method

The New Zealand housing sector is experiencing rapid growth that has a significant impact on society, the economy, and the environment. In line with the growth, the housing market for both residential and business purposes has been booming, as have house prices. To sustain the housing development, it is critical to accurately monitor and predict housing prices so as to support the decision-making process in the housing sector. This study is devoted to applying a mathematical method to predict housing prices. The forecasting performance of two types of models: autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) analysis are compared. The ARIMA and regression models are developed based on a training-validation sample method. The results show that the ARIMA model generally performs better than the regression model. However, the regression model explores, to some extent, the significant correlations between house prices in New Zealand and the macro-economic conditions.

scholarly journals Forecasting Inflation: Autoregressive Integrated Moving Average Model

2016 ◽  
Vol 12 (1) ◽  
pp. 83 ◽  
Author(s):  
Muhammad Iqbal ◽  
Amjad Naveed
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Arima Models ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Quarterly Data ◽  
Forecasting Performance ◽  
Moving Average Model ◽  
Forecasting Inflation

This study compares the forecasting performance of various Autoregressive integrated moving average (ARIMA) models by using time series data. Primarily, The Box-Jenkins approach is considered here for forecasting. For empirical analysis, we used CPI as a proxy for inflation and employed quarterly data from 1970 to 2006 for Pakistan. The study classified two important models for forecasting out of many existing by taking into account various initial steps such as identification, the order of integration and test for comparison. However, later model 2 turn out to be a better model than model 1 after considering forecasted errors and the number of comparative statistics.

scholarly journals Application of SARIMA Model for Forecasting Consumption of Electricity in Gezira State, Sudan (2006-2018): تطبيقات نماذج ساريما للتنبؤ باستهلاك الكهرباء في ولاية الجزيرة, السودان  (2006- 2018)

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Nada Mohammed Ahmed Alamin
Keyword(s):  
Historical Data ◽  
Moving Average ◽  
Electricity Consumption ◽  
Seasonal Behavior ◽  
Sarima Model ◽  
Control Center ◽  
Autoregressive Integrated Moving Average ◽  
Good Forecast ◽  
National Control ◽  
Monthly Electricity Consumption

    The purpose of the research is to reach the forecast of monthly electricity consumption in Gezira state, Sudan for the period (Jun 2018 - Dec 2020) through the application to the historical data of electric power consumption (Jan 2006-May 2018) obtained from the National Control Center, which has been applied in the research methodology of seasonal Autoregressive Integrated Moving Average due to seasonal behavior in the data, good forecast has been given by SARIMA (2, 1, 7) (0, 1, 1), which has been examined its quality using the Thiel coefficient. The study recommended the use of the model of seasonal Autoregressive Integrated Moving Average in data with Seasonal behavior due to its simple application and accuracy of the results reached.    

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