Time Series of Rainfall Model with Markov Switching Autoregressive

Devianto, Dodi; Maiyastri,; Wisza, Uqwatul Alma; Wara, Michelin; Permathasari, Putri; Zen, Rika Okda Marlina

doi:10.1109/icaiti.2018.8686745

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…The self-regression model for Markov was proposed by Hamilton [39] (James D [39]. The autoregressive model for Markov with order p is considered in the following two cases [40],James D [39], James D. [38]:…”

Section: The Markov Switching Regression Modelmentioning

confidence: 99%

Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran

Javari

2021

SN Appl. Sci.

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This paper represents the recurrence (reoccurrence) changes in the rainfall series using Markov Switching models (MSM). The switching employs a dynamic pattern that allows a linear model to be combined with nonlinearity models a discrete structure. The result is the Markov Switching models (MSM) reoccurrence predicting technique. Markov Switching models (MSM) were employed to analyze rainfall reoccurrence with spatiotemporal regime probabilities. In this study, Markov Switching models (MSM) were used based on the simple exogenous probability frame by identifying a first-order Markov process for the regime probabilities. The Markov transition matrix and regime probabilities were used to analyze the rainfall reoccurrence in 167 synoptic and climatology stations. The analysis results show a low distribution from 0.0 to 0.2 (0–20%) per day spatially from selecting stations, probability mean of daily rainfall recurrence is 0.84, and a different distribution based on the second regime was found to be more remarkable to the rainfall variability. The rainfall reoccurrence in daily rainfall was estimated with relatively low variability and strong reoccurrence daily with ranged from 0.851 to 0.995 (85.1–99.5%) per day based on the spatial distribution. The variability analysis of rainfall in the intermediate and long variability and irregular variability patterns would be helpful for the rainfall variability for environmental planning.

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Section: The Markov Switching Regression Modelmentioning

confidence: 99%

Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran

Javari

2021

SN Appl. Sci.

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“…While the financial performance of Islamic Rural Banks can be applied by using clustering analysis as described in [3]. In other financial cases, time series of rainfall predicted by using Markov Autoregressive as in [4].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model of Seasonal ARIMA-ANN to Forecast Tourist Arrivals through Minangkabau International Airport

Yollanda¹,

Devianto²

2020

Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bog

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The number of tourist arrivals forecasting is required for the future development of tourism industry to improve the economic growth. The number tourist arrivals data can be analyzed by building a model so that it will help to find out the number of tourist arrivals in the next period which is through Minangkabau International Airport. The linear model that is used is Seasonal Autoregressive Integrated Moving Average (SARIMA) used and continued to build a nonlinear model of the residual SARIMA model using Artificial Neural Network (ANN). In this research, SARIMA model which obtained is SARIMA (1, 0, 1) (1, 1, 0) 12. But, residual of SARIMA model has not been fulfilled an autocorrelation assumption so that it isproposed a new model of SARIMA-ANN. The residual model of SARIMA is built using the ANN model architecture with 2-2-2-1 network topology. The performance rate of time series model of tourist arrivals which is the data started on January 2012 until March 2019 is measured using Mean Absolute Percentage Error (MAPE). Based on the MAPE value of 17.1770% indicates that the model obtained having good performance to forecast the number of tourist arrivals through Minangkabau International Airport in the future.

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The forecasting model of Bitcoin price with fuzzy time series Markov chain and chen logical method

Ramadani¹,

Devianto²

2020

AIP Conference Proceedings

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Time Series of Rainfall Model with Markov Switching Autoregressive

Cited by 4 publications

References 19 publications

Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran

Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran

Hybrid Model of Seasonal ARIMA-ANN to Forecast Tourist Arrivals through Minangkabau International Airport

The forecasting model of Bitcoin price with fuzzy time series Markov chain and chen logical method

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