On Modelling Seasonal Arima Series: Application on Road Accidents in Northeast Algeria

Merabet, Farida; Zeghdoudi, Halim

doi:10.37394/23203.2020.15.25

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…When we have a stationary seasonal time series with sample ACF and sample partial ACF that have patterns that are akin to the theoretical ACF and sample partial ACF, we are expected to fit the INAR model to the data. In situations where, the sample ACF and sample partial ACF do not look exactly like their theoretical counterparts, a variety of models can be fitted to the given data and the best model is determined using model selection criteria 24 .…”

Section: The General Seasonal Inar Modelmentioning

confidence: 99%

Some developments on seasonal INAR processes with application to influenza data

Almuhayfith,

Okereke,

Awale

et al. 2023

Sci Rep

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Influenza epidemic data are seasonal in nature. Zero-inflation, zero-deflation, overdispersion, and underdispersion are frequently seen in such number of cases of disease (count) data. To explain these counts’ features, this paper introduces a flexible model for nonnegative integer-valued time series with a seasonal autoregressive structure. Some probabilistic properties of the model are discussed for general seasonal INAR(p) model and three estimation methods are used to estimate the model parameters for its special case seasonal INAR(1) model. The performance of the estimation procedures has been studied using simulation. The proposed model is applied to analyze weekly influenza data from the Breisgau- Hochschwarzwald county of Baden–Württemberg state, Germany. The empirical findings show that the suggested model performs better than existing models.

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Section: The General Seasonal Inar Modelmentioning

confidence: 99%

Some developments on seasonal INAR processes with application to influenza data

Almuhayfith,

Okereke,

Awale

et al. 2023

Sci Rep

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show abstract

“…Investors can make more accurate risk management, option pricing, and portfolio optimization decisions with the help of GARCH models when they use well-chosen parameters to estimate and forecast volatility ( Chu & Freund, 1996 ; Molnár, 2016 ). The ARIMA model is widely used for time series forecasting since it provides for both autoregressive and moving average properties ( Shah, Bhatt & Shah, 2022 ; Merabet & Zeghdoud, 2020 ). Effective time series modeling relies heavily on the careful selection of ARIMA model parameters, such as the order of autoregressive (p), integrated (d), and moving average (q) terms ( Poddar et al, 2020 ; Kumar, Kumar & Kumar, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction

Bagalkot,

Naik

2024

PeerJ Computer Science

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Stock price data often exhibit nonlinear patterns and dynamics in nature. The parameter selection in generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) models is challenging due to stock price volatility. Most studies examined the manual method for parameter selection in GARCH and ARIMA models. These procedures are time-consuming and based on trial and error. To overcome this, we considered a GWO method for finding the optimal parameters in GARCH and ARIMA models. The motivation behind considering the grey wolf optimizer (GWO) is one of the popular methods for parameter optimization. The novel GWO-based parameters selection approach for GARCH and ARIMA models aims to improve stock price prediction accuracy by optimizing the parameters of ARIMA and GARCH models. The hierarchical structure of GWO comprises four distinct categories: alpha (α), beta (β), delta (δ) and omega (ω). The predatory conduct of wolves primarily encompasses the act of pursuing and closing in on the prey, tracing the movements of the prey, and ultimately launching an attack on the prey. In the proposed context, attacking prey is a selection of the best parameters for GARCH and ARIMA models. The GWO algorithm iteratively updates the positions of wolves to provide potential solutions in the search space in GARCH and ARIMA models. The proposed model is evaluated using root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE). The GWO-based parameter selection for GARCH and ARIMA improves the performance of the model by 5% to 8% compared to existing traditional GARCH and ARIMA models.

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Rainfall prediction climatological station of Banjarbaru using arima kalman filter

Mehta

Sukmawaty

Khairullah

2021

J. Phys.: Conf. Ser.

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Time series analysis is a method built in a particular time sequence for prediction. One of the models in time series analysis used for prediction is the ARIMA model introduced by Box and Jenkins. As time goes by, the ARIMA model was developed by applying algorithms, one of which was the Kalman Filter algorithm. This study aims to estimate the parameters of the ARIMA model used as the Kalman Filter’s initial value to forecast rainfall using ARIMA and ARIMA Kalman Filter. Determination of the ARIMA model is done by dividing the data into training and testing. The results obtained from the three training data have the same model, namely ARIMA (0,0,0) × (0,1,1)12 models but with different parameter values than those used as initial values for the Kalman Filter. The results obtained using the ARIMA model with Kalman Filter significantly affect the initial data of 90% training data model parameters with an RMSE value of 155,13. Then predictions are made, the results obtained by ARIMA Kalman Filter can follow the actual data, but from June to October, the prediction results cannot approach the actual data. According to events in the field, June to October is the dry season, where rainfall is deficient

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On Modelling Seasonal Arima Series: Application on Road Accidents in Northeast Algeria

Cited by 6 publications

References 20 publications

Some developments on seasonal INAR processes with application to influenza data

Some developments on seasonal INAR processes with application to influenza data

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction

Rainfall prediction climatological station of Banjarbaru using arima kalman filter

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