PEMODELAN DAN PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN ARIMAX-TARCH

The Composite Stock Price Index (IHSG) is a value that describes the combined performance of all shares listed on the Indonesia Stock Exchange. JCI serves as a benchmark for investors in investing. The method used to predict future conditions based on past data is forecasting . Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) is amodel time series that can be used for forecasting. Financial data has high volatility which causes the variance of the residual model which is not constant (heteroscedasticity). ARCH / GARCH model is used to solve the heteroscedasticity problem in the model. If the data is heteroscedastic and asymmetric, then the model can be used Threshold Autoregressive Conditional Heteroskedasticity (TARCH). The data used are the Composite Stock Price Index (IHSG) for the January 2000 - April 2020 period and the dollar exchange rate data for the January 2000 - April 2020 period asvariables independent from the ARIMAX model. The best model used to predict the JCI from the results of this study is the ARIMAX (1,1,0) -TARCH (1,2) model with an AIC value of -0.819074.

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.

Determining the Pension Benefit Obligation of a Defined Benefit Plan: Applying a Multivariate ARIMA Stochastic Model

<p>In this study we examine the robustness of fit for a multivariate and an autoregressive integrated moving average model to a data sample time series type. The sample is a recurrent actuarial data set for a 10-year horizon. We utilize this methodology to contrast with stochastic models to make projections beyond the data horizon. Our key results suggest that both types of models are useful for making predictions of actuarial liability levels given by PBO Projected Benefit Obligations on and off the horizon of the sample time series. As we have seen in prior research, the use of multivariate models for control and auditing purposes is widely recommended. Fast and reliable statistical estimates are desirable in all cases, whether for audit purposes or to verify and validate miscellaneous actuarial results.</p>

Forecasting Stock Price PT. Telkom Using Hybrid Time Series Regression Linear– Autoregressive Integrated Moving Average Model

The hybrid method is a method of combining two forecasting models. Hybrid method is used to improve forecasting accuracy. In this study, the Time Series Regression (TSR) linear model will be combined with the Autoregressive Integrated Moving Average (ARIMA) model. The TSR linear model is used to obtain the model and residual value, then the residual value of the TSR linear model will be modeled by the ARIMA model. This combination method will produce a hybrid TSR linear-ARIMA model. The case study in this research is stock closing price (daily) of PT. Telkom Indonesia Tbk. The stock closing price (daily) of PT. Telkom Indonesia Tbk in 2020 showed an decreasing and increasing trend pattern. The results of this study obtained the best model of hybrid TSR linear-ARIMA (2,1,1) with the proportion of data training and testing is 70:30. In the best model, the MAD value is 56.595, the MAPE value is 1.880%, and the RMSE value is 78.663. It is also found that the hybrid TSR linear-ARIMA model has a smaller error value than the TSR linear model. The results of forecasting the stock price of PT. Telkom Indonesia Tbk for the period 02 January 2021 to 29 January 2021 formed a decreasing trend pattern.

Application of the Autoregressive Integrated Moving Average Exogenous (ARIMAX) with Calendar Variation Effect Method for Forecasting Chocolate Data in Indonesia and the United States

Forecasting chocolate consumption is required by producers in preparing the amount of production each month. The tradition of Valentine, Christmas and Eid al-Fitr which are closely related to chocolate makes it impossible to predict chocolate by using the Classical Time Series method. Especially for Eid al-Fitr, the determination follows the Hijri calendar and each year advances 10 days on the Masehi calendar, so that every three years Eid al-Fitr will occur in a different month. Based on this, the chocolate forecasting will show a variation calendar effect. The method used in modeling and forecasting chocolate in Indonesia and the United States is the ARIMAX (Autoregressive Integrated Moving Average Exogenous) method with Calendar Variation effect. As a comparison, modeling and forecasting are also carried out using the Naïve Trend Linear, Naïve Trend Exponential, Double Exponential Smoothing, Time Series Regression, and ARIMA methods. The ARIMAX method with Calendar Variation Effect produces a very precise MAPE value in predicting chocolate data in Indonesia and the United States. The resulting MAPE value is below 10 percent, so it can be concluded that this method has a very good ability in forecasting.

A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting

This article aims to propose a novel hybrid forecasting model involving autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means clustering. The single models and k-means clustering are used to build the hybrid forecasting models in different levels of complexity (i.e. ARIMA; hybrid model of ARIMA and ANNs; and hybrid model of k-means, ARIMA, and ANN). To obtain the final forecasting value, the forecasted values of these three models are combined with the weights generated from the discount mean square forecast error (DMSFE) method. The proposed model is applied to three well-known data sets: Wolf's sunspot, Canadian lynx and the exchange rate (British pound to US dollar) to evaluate the prediction capability in three measures (i.e. MSE, MAE, and MAPE). In addition, the prediction performance of the proposed model is compared to ARIMA; ANNs; Khashei and Bijari's model; and the hybrid model of k-means, ARIMA, and ANN. The obtained results show that the proposed model gives the best performance in MSE, MAE, and MAPE for all three data sets.

Modeling the Smart Factory Manufacturing Products Characteristics: The Perspective of Energy Consumption

Economic progress is built on the foundation of energy. In the industrial sector, smart factory energy consumption analysis and forecasts are crucial for improving energy consumption rates and also for creating profits. The importance of energy analysis and forecasting in an industrial environment is increasing speedily. It is a great chance to provide a technical boost to smart factories looking to reduce energy usage and produce more profit through the control and optimization modeling. It is tough to analyze energy usage and make accurate estimations of industrial energy consumption. Consequently, this study examines monthly energy consumption to identify the discrepancy between energy usages and energy needs. It depicts the link between energy consumption, demand, and various industrial goods by pattern recognition. The correlation technique is utilized in this study to figure out the link between energy usage and the weight of various materials used in product manufacturing. Next, we use the moving average approach to calculate the monthly and weekly moving averages of energy usages. The use of data-mining techniques to estimate energy consumption rates based on production is increasingly prevalent. This study uses the autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) to compare the actual data with forecasting data curves to enhance energy utilization. The Root Mean Square Error (RMSE) performance evaluation result for ARIMA and SARIMA is 8.70 and 10.90, respectively. Eventually, the Variable Important technique determines the smart factory’s most essential product to enhance the energy utilization rate and obtain profitable items for the smart factory.

Emergency department visits for self-harm in adolescents after release of the Netflix series ‘13 Reasons Why’

Objective: To determine whether the release of the first season of the Netflix series ‘13 Reasons Why’ was associated with changes in emergency department presentations for self-harm. Methods: Healthcare utilization databases were used to identify emergency department and outpatient presentations according to age and sex for residents of Ontario, Canada. Data from 2007 to 2018 were used in autoregressive integrated moving average models for time series forecasting with a pre-specified hypothesis that rates of emergency department presentations for self-harm would increase in the 3-month period following the release of 13 Reasons Why (1 April 2017 to 30 June 2017). Chi-square and t tests were used to identify demographic and health service use differences between those presenting to emergency department with self-harm during this epoch compared to a control period (1 April 2016 to 30 June 2016). Results: There was a significant estimated excess of 75 self-harm-related emergency department visits (+6.4%) in the 3 months after 13 Reasons Why above what was predicted by the autoregressive integrated moving average model (standard error = 32.4; p = 0.02); adolescents aged 10–19 years had 60 excess visits (standard error = 30.7; p = 0.048), whereas adults demonstrated no significant change. Sex-stratified analyses demonstrated that these findings were largely driven by significant increases in females. There were no differences in demographic or health service use characteristics between those who presented to emergency department with self-harm in April to June 2017 vs April to June 2016. Conclusions: This study demonstrated a significant increase in self-harm emergency department visits associated with the release of 13 Reasons Why. It adds to previously published mortality, survey and helpline data collectively demonstrating negative mental health outcomes associated with 13 Reasons Why.