Spatial Vector Autoregressive Model with Calendar Variation for East Java Inflation and Money Supply

Sumarminingsih, Eni; Matoha, Setiawan; Suharsono, Agus; Ruchjana, Budi Nurani

doi:10.18576/amis/120610

Cited by 6 publications

(2 citation statements)

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“…5. Parameter estimation of the SpVAR (1,p) will be carried out by applying the FIML approach (Sumarminingsih et al, 2018). The SpVAR model is included in the multivariate time series model, which analyzes space-time data with more than one variable.…”

Section: Methodsmentioning

confidence: 99%

Reconstruction of Rainfall Patterns with the SpVAR Method: Spatial Analysis in DKI Jakarta

Melanwati,

Sumarminingsih,

Pramoedyo

2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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Unexpected rainfall is often a challenge for urban areas such as DKI Jakarta. Therefore, this study aims to establish a Spatial Vector Autoregressive (SpVAR) model to analyze rainfall data in DKI Jakarta from 2017 to 2021. This study used three endogenous variables: the amount of rainfall, air temperature and humidity. The use of the SpVAR method with uniform spatial weighting in the DKI Jakarta area was chosen to provide an initial picture of the potential for spatial interactions between various locations in a complex climate context. This method provides valuable insight into the possibility of spatial dependence during climate change in DKI Jakarta. The SpVAR (1.3) model is based on the VAR (p) model by limiting the spatial orders to one. Parameters of the SpVAR model (1.3) were estimated using the FIML method to identify significant factors in the influence of rainfall in the region. The results showed that the SpVAR model (1.3) shows that rainfall, air temperature and humidity in one location are affected by the same variables in other locations. However, not all of them significantly affect five areas in DKI Jakarta Province. This study confirms the effectiveness of the SpVAR method in analyzing spatial patterns of rainfall, provides essential insights for understanding climate, and supports decision-making that is more responsive to urban disasters in the future.

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Section: Methodsmentioning

confidence: 99%

Reconstruction of Rainfall Patterns with the SpVAR Method: Spatial Analysis in DKI Jakarta

Melanwati,

Sumarminingsih,

Pramoedyo

2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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“…The data with calendar variation effects have periodic and recurring patterns but with varying lengths. This kind of data is usually found in the monthly number of tourist arrivals (Sulandari et al, 2021), hourly electricity load time series , monthly inflow and outflow currency data in Bank Indonesia (Suhartono et al, 2019), inflation and money supply (Sumarminingsih et al, 2018), and monthly sales of clothes (Lee & Hamzah, 2010).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Tourist Arrivals to Sangiran Using Fuzzy with Calendar Variations

Sulandari

Yudhanto

Subanti

et al. 2022

Advances in Hospitality and Tourism Research (AHTR)

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Fuzzy method has been widely used in time series forecasting. However, the current fuzzy time models have not accommodated the holiday effects so that the forecasting error becomes large at certain moments. Regarding the problem, this study proposes two algorithms, extended of Chen's and seasonal fuzzy time series method (FTS), to consider the holiday effect in forecasting the monthly tourist arrivals to ancient human Sangiran Museum. Both algorithms consider the relationship between Eid holidays as the effect of calendar variations. The forecasting results obtained from the two proposed algorithms are then compared with those obtained from the Chen's and the seasonal FTS. Based on the experimental results, the proposed method can reduce mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) obtained from Chen's method up to 61%, 61%, and 58%, respectively. Moreover, compared to that obtained from the seasonal FTS, the proposed method can reduce the MAE, RMSE, and MAPE values up to 35%, 36%, and 29%, respectively. The method proposed in this paper can be implemented to other time series with seasonal pattern and calendar variation effects.

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Spatial Vector Autoregressive with Metric Exogenous Variable (SpVARX) for Inflation and Outflow Forecasting

Sohibien,

Setiawan,

Prastyo

2024

Procedia Computer Science

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Spatial Vector Autoregressive Model with Calendar Variation for East Java Inflation and Money Supply

Cited by 6 publications

References 0 publications

Reconstruction of Rainfall Patterns with the SpVAR Method: Spatial Analysis in DKI Jakarta

Reconstruction of Rainfall Patterns with the SpVAR Method: Spatial Analysis in DKI Jakarta

Forecasting Tourist Arrivals to Sangiran Using Fuzzy with Calendar Variations

Spatial Vector Autoregressive with Metric Exogenous Variable (SpVARX) for Inflation and Outflow Forecasting

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