Application of ARIMAX Model to Forecast Weekly Cocoa Black Pod Disease Incidence

Ling, Ashley; Darmesah, G.; Chong, Khim Phin; Ho, Chuong

doi:10.13189/ms.2019.070705

Cited by 14 publications

(1 citation statement)

References 10 publications

(14 reference statements)

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“…The ARIMAX (Autoregressive Integrated Moving Average with Exogenous Input) model is an ARIMA model with the addition of predictor variables (Wei, 2006). Predictor variables can be factors that are thought to have a significant effect and dummy variables (Ling., Darmesah, Chong, & Ho, 2019). There are several steps in identifying the ARIMAX model.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Health Sector Stock Prices using ARIMAX Method

Aprilianto

Hasibuan

Harahap

2022

SinkrOn

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In daily stock trading activities, stock prices can experience ups and downs. The rise and fall of stock prices occurs due to changes in supply and demand for these shares. The COVID-19 pandemic did not have a negative effect, instead it had a positive impact on stock prices in health companies. companies in the health sector experienced a fairly good profit of 10.46% in the fourth quarter of 2021. This fact made investors interested in buying shares in companies in the health sector in the hope of selling them when demand increased, resulting in doubled profits. Stock conditions continue to fluctuate every day, making investors need to pay attention and study the past data of the health sector company that will be selected before deciding to invest. Therefore, it is necessary to forecast stock prices in the health sector for the next several periods as a step in making investment decisions. The health sector companies that will be modeled are PT Kimia Farma (Persero) Tbk and PT Kalbe Farma Tbk. The method used in this study is the ARIMAX model. The test and analysis results show that based on the RMSE and MAPE values, the best model is ARIMAX(5,13) for PT Kalbe Farma Tbk shares with a MAPE value of 1% in in-sample data and 0.6% in out-sample data.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting Health Sector Stock Prices using ARIMAX Method

Aprilianto

Hasibuan

Harahap

2022

SinkrOn

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Nowcasting influenza‐like illness (ILI) via a deep learning approach using google search data: An empirical study on Taiwan ILI

Zhao

Chen³

et al. 2021

Int J of Intelligent Sys

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Influenza outbreaks have brought increasing challenges to public health systems globally. The effective and efficient tracking of influenza can help authorities make informed and proactive decisions. In this study, we focus on nowcasting influenza epidemics at regional level. To alleviate the information lag between the release of the Centers for Disease Control's influenza‐like illness (ILI) reports and real‐time influenza activity, we incorporate Google search data and holiday effects to facilitate the ILI nowcasting. We develop a deep learning framework by extending the spatiotemporal residual network (ST‐ResNet) to nowcast ILI rates in irregular‐shaped region. We investigate the effect of temporal and spatial dependencies among irregular‐shaped regions as well as external influence on ILI nowcasting. Various forecasting models, including time series models, penalized regression models, and other deep learning models, are employed for evaluating the performance of the proposed framework. Moreover, based on city‐level ILI data in Taiwan, we conduct extensive experiments for methods validation. The results show that the extended ST‐ResNet can effectively capture the complex spatiotemporal dependencies of ILI activity and the effects of external variables. Additionally, the strategy of incorporating Google search data and holiday effects improves the prediction. The findings may provide insights into the utility of various statistical and deep learning methods for effective ILI tracking at regional level and facilitate informed decision making for public health authorities.

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