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

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.

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

Journal of the Bangladesh Agricultural University ◽

10.3329/jbau.v11i2.19925 ◽

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

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

PLoS ONE ◽

10.1371/journal.pone.0246377 ◽

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.

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SSolar-GOA v1.0: a simple, fast, and accurate Spectral SOLAR radiative transfer model for clear skies

10.5194/gmd-2021-178 ◽

2021 ◽

Author(s):

Victoria Eugenia Cachorro ◽

Juan Carlos Antuña-Sánchez ◽

Ángel Máximo de Frutos

Keyword(s):

Radiative Transfer ◽

Ground Level ◽

Environmental Research ◽

Radiative Transfer Model ◽

Homogeneous Layer ◽

Band Model ◽

Transfer Model ◽

Atmospheric Conditions ◽

Irradiance Data ◽

Relative Differences

Abstract. The aim of this work is to describe the features and to validate a simple, fast, accurate and physical-based spectral radiative transfer model in the solar wavelength range under clear skies. The model, named SSolar-GOA (the first “S” stands for “Spectral”), was developed to evaluate the instantaneous values of spectral solar irradiances at ground level. The model data output are well suited to work at a spectral resolution of 1–10 nm, are adapted to commercial spectroradiometers or filter radiometers, and are addressed to a wide community of users for many different applications (atmospheric and environmental research studies, remote sensing, solar energy, agronomy/forestry, ecology, etc.). The model requirements are designed based on the simplicity of the analytical expressions for the transmittance functions in order to be easily replicated and applied by users. Although spectral, the model runs quickly and has sufficient accuracy. The model assumes a single mixed molecule-aerosol scattering layer where the original Ambartsumian method of “adding layers" in a one-dimensional medium is applied, obtaining a parameterized expression for the total transmittance of scattering. Absorption by the different atmospheric gases follows “band model” parameterized expressions. Both processes are applied to a single atmospheric homogeneous layer as necessary approaches for developing a simple model under the consideration of non-interaction. Besides, the input parameters must be realistic and easily available since the spectral aerosol optical depth (AOD) is the main driver of the model. The validation of the SSolar-GOA model has been carried out through extensive comparison with simulated irradiance data from the LibRadtran package and with direct/global spectra measured by spectroradiometers. Thousands of spectra under clear skies have been compared for different atmospheric conditions and solar zenithal angles (SZA). From the results of the comparison with LibRadtran, the SSolar-GOA model shows a high performance for the entire solar spectral range for direct, global, and diffuse spectral components with relative differences of +1 %, +3 %, and 8 %, respectively, and our model always gives an underestimation. Compared with the measured irradiance data of the Licor1800 and ASD spectroradiometers, the relative differences of direct and global components are within the overall experimental error (about ±2–12 %) with underestimated or overestimated values. The diffuse component presents the highest degree of difference which can reach ±20–30 %. Obviously, the relative differences depend strongly on the spectral solar region analysed and the SZA. Model approach errors combined with calibration instrument errors may explain the observed differences.

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

The Canadian Journal of Psychiatry ◽

10.1177/07067437211070650 ◽

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.

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Forecasting Sports Popularity: Application of Time Series Analysis

Academic Journal of Interdisciplinary Studies ◽

10.1515/ajis-2017-0009 ◽

2017 ◽

Vol 6 (2) ◽

pp. 75-82 ◽

Cited By ~ 1

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.

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

European Journal of Mathematics and Statistics ◽

10.24018/ejmath.2021.2.6.80 ◽

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.

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

International Journal on Information and Communication Technology (IJoICT) ◽

10.21108/ijoict.v7i1.554 ◽

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.

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Peramalan Indeks Harga Konsumen dengan Metode Singular Spectral Analysis (SSA) dan Seasonal Autoregressive Integrated Moving Average (SARIMA)

Jurnal Matematika MANTIK ◽

10.15642/mantik.2017.3.2.74-82 ◽

2017 ◽

Vol 3 (2) ◽

pp. 74-82

Author(s):

Deltha Airuzsh Lubis ◽

Muhamad Budiman Johra ◽

Gumgum Darmawan

Keyword(s):

Spectral Analysis ◽

Price Index ◽

Moving Average ◽

Consumer Price Index ◽

Parametric Model ◽

Seasonal Patterns ◽

Autoregressive Integrated Moving Average ◽

Goods And Services ◽

High Inflation ◽

Sarima Models

Consumer Price Index (CPI) are the indicators used to measure the inflation and deflation of a group of goods and services in general. Forecasting CPI to be important as early detection in facing price hikes. This study uses the SSA and SARIMA. SARIMA a parametric model that requires various assumptions while SSA is a nonparametric technique that is free from a variety of assumptions, but both methods require seasonal patterns in the data. Based on the research results, methods of SSA with length window(L) of 24 and a grouping of 4 (1 group of seasonal and 3 groups of trends) and SARIMA models of order (0,1,1), (0,1,1) 6 is the most accurate and reliable models in forecasting CPI to the value Padang Sidempuan City. Forecasting CPI Padang Sidempuan City for the next 5 months with SSA method and SARIMA (0,1,1), (0,1,1) 6 shows the pattern of a trend is likely to increase but forecasting the 5th month with SSA method showed a surge in the value of CPI high or high inflation will occur.

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