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

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.

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

Model Variasi Kalender pada Regresi Runtun Waktu untuk Peramalan Jumlah Pengunjung Grojogan Sewu

<p><strong>Abstract.</strong> Grojogan Sewu visitors experience a significant increase during school holidays, year-end holidays, and also Eid al-Fitr holidays. The determination of Eid Al-Fitr uses the Hijriyah calendar so that the occurrence of Eid al-Fitr will progress 10 days when viewed from the Gregorian calendar, this causes calendar variations. The objective of this paper is to apply a calendar variation model based on time series regression and SARIMA models for forecasting the number of visitors in Grojogan Sewu. The data are Grojogan Sewu visitors from January 2009 until December 2019. The results show that time series regression with calendar variation yields a better forecast compared to the SARIMA model. It can be seen from the value of root mean square error (<em>RMSE</em>) out-sample of time series regression with calendar variation is less than of SARIMA model.</p><p><strong>Keywords: </strong>Calendar variation, time series regression, SARIMA, Grojogan Sewu</p>

The Inflation Forecasting of Major Cities In East Kalimantan: A Comparison Of Holt-Winters And SARIMA Model

This research aims to compare the performance of Holt Winters and Seasonal Autoregressive Integrate Moving Average (SARIMA) models in predicting inflation in Balikpapan and Samarinda, two biggest cities in East Kalimantan province. The importance of East Kalimantan province cannot be overstated since it has been declared as the venue for the capital of Indonesia. Hence, inflation prediction of the two cities will give valuable insights about the economic nature of the province for the country’s new capital. The data used in this study extended from January 2015 to September 2021. The data were divided into training and test data. The training data were used to model the time series equation using Holt winters and SARIMA models. Later, the models derived from training data were employed to produce forecasts. The forecasts were compared to the actual inflation data to determine the appropriate model for forecasting. Test data were from January 2015 to December 2020 and test data extended from January 2021 to September 2021. The result showed that Holt-Winters performed better than SARIMA in prediction inflation. The Root Mean Squared Error (RMSE) values are lower for Holt-Winters Exponential Smoothing for both cities. It also predicts better timing of cyclicality than SARIMA model.

Single Versus Combined Forecasting: The Case Of Wind Run Data

This article compared single to combined forecasts of wind run using artificial neural networks, decomposition, Holt-Winters’ and SARIMA models. The artificial neural networks utilized the feedback framework while decomposition and Holt-Winters’ approaches utilized their multiplicative versions. Holt-Winters’ performed best of single models but ranked fourth, of all fifteen models (single and combined). The combination of decomposition and Holt-Winters’ models ranked best of all two-model combinations and second of all models. Combination of decomposition, Holt-Winters’ and SARIMA performed best of three-model combinations and ranked first, of all models. The only combination of four models ranked third of all models. The accuracy of single forecast should not be underestimated as a single model (Holt-Winters’) outperformed eleven combined models. It is therefore, evident that inclusion of additional model forecast does not necessarily improve combined forecast accuracy. In any modeling situation, single and combined forecasts should be allowed to compete.

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

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.

Application of Sarima Models in Modelling and Forecasting Monthly Rainfall in Nigeria

Application of SARIMA model in modelling and forecasting monthly rainfall in Nigeria was considered in this study. The study utilizes the Nigerian monthly rainfall data between 1980-2015 obtained from World Bank Climate Portal. The Box-Jenkin’s methodology was adopted. SARIMA (2,0,1) (2,1,1) [12] was the best model among others that fit the Nigerian rainfall data (1980-2015) with maximum p-value from Box-Pierce Residuals Test. The study forecasts Nigeria’s monthly rainfall from 2018 through 2042. It was discovered that the month of April is the period of onset of rainfall in Nigeria and November is the period of retreat. Based on the findings, Nigeria will experience approximately equal amount of rainfall between 2018 to 2021 and will experience a slight increase in rainfall amount in 2022 to about 1137.078 (mm). There will be a decline of rainfall at 2023 to about 1061 (mm). Rainfall values will raise again to about 1142.756 (mm) in 2024 and continue to fluctuate with decrease in variation between 2024 to 2042, then remain steady to 2046 at approximately 1110.0 (mm). Nigerian Government should provide a more mechanized and drier season farming methods to ease the outage of rainfall in future that may be caused due to natural (or unpredictable) variation.

Comparison between SARIMA and Holt–Winters models for forecasting monthly streamflow in the western region of Cuba

The present study aims to compare SARIMA and Holt–Winters model forecasts of mean monthly flow at the V Aniversario basin, western Cuba. Model selection and model assessment are carried out with a rolling cross-validation scheme using mean monthly flow observations from the period 1971–1990. Model performance is analyzed in one- and two-year forecast lead times, and comparisons are made based on mean squared error, root mean squared error, mean absolute error and the Nash–Sutcliffe efficiency; all these statistics are computed from observed and simulated time series at the outlet of the basin. The major findings show that Holt–Winters models had better performance in reproducing the mean series seasonality when the training observations were insufficient, while for longer training subsets, both models were equally competitive in forecasting one year ahead. SARIMA models were found to be more reliable for longer lead-time forecasts, and their limitations after being trained on short observation periods are due to overfitting problems. Article Highlights Comparison based on rolling cross-validation revealed the models forecasts sensibility to available observations amount. HW and SARIMA models perform better when limited observations or long-view forecasting, respectively, otherwise they do similar. HW models were superior modeling less variable monthly flows while SARIMA models better forecast the highly variable periods.

Sarima Modeling of Monthly Temperature in the Northern part of Ghana

The Sarima model is used in this study to forecast the monthly temperature in Ghana's northern region. The researchers used temperature data from January 1990 to December 2020. The temperature data was found to be stationary using the Augmented Dickey Fuller (ADF) test. The ACF and PACF plots proposed six SARIMA models: SARIMA (1,0,0) (1,0,0) (12), SARIMA (2,0,0) (1,0,0) (12), SARIMA (1,0,1) (1,0,0) (12), SARIMA (0,0,1) (1,0,0) (12), SARIMA (0,0,1) (0,0,1) (12), SARIMA (0,0,1) (0,0,1) (12). The best model was chosen based on the lowest Akaike Information Criteria (AICs) and Bayesian Information Criteria (BIC) values. The Ljung-Box data, among others, were used to determine the model's quality. All diagnostic tests are passed by the SARIMA (1,0,0) (1,0,0) (12) model. As a result, the SARIMA (1,0,0) (1,0,0) (12) is the best-fitting model for predicting monthly temperatures in Ghana's northern region.

Comparison between the Holt-Winters and SARIMA Models in the Prediction of NDVI in an Arid Region in Kenya using Pixel-wise NDVI Time Series

Desertification is major issue in arid and semi-arid lands (ASAL) with devastating environmental and socio-economic impacts. Time series analysis was applied on 19 years’ pixel-wise monthly mean Normalized Difference Vegetation Index (NDVI) data. The aim of this study was to identify a time series model that can be used to predict NDVI at the pixel level in an arid region in Kenya. The Holt-Winters and Seasonal Auto Regressive Integrated Moving Average (SARIMA) models were developed and statistical analysis was carried out using both models on the study area. We performed a grid search to optimise and determine the best hyper parameters for the models. Results from the grid search identified the Holt-Winters model as an additive model and a SARIMA model with a trend autoregressive (AR) order of 1, a trend moving average (MA) order of 1 and a seasonal MA order of 2, with both models having a seasonal period of 12 months. It was concluded that the Holt-Winters model showed the best performance for 600 ✕ 600 pixels (MAE = 0.0744, RMSE = 0.096) compared to the SARIMA model.