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2022•AIP conference proceedingsRequires access

Modeling and forecasting using auto regressive integrated moving average

E. Priyadarshini, E. Sharon Preethi, M. Vidhya, Samuel Chakkravarthi, A. Govindarajan

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Abstract

In this paper ,Autocorrelation (ACF) and Partial autocorrelation (PACF) functions had been envisioned to help in identifying appropriate orders of the Autoregressive model of order p (AR) and Moving Average model of order q (MA). The Bayesian Information Criteria (BIC) test was done on numerous models and it was found the model ARIMA (1, 1, 0) has the least BIC value. Ljung Q statistics data showed that there was no serial correlation across all the models. The standard error measures such as MSE, MAE, MAPE was calculated for the forecasting rates and was found to be minimal.

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What this paper is about

In this paper ,Autocorrelation (ACF) and Partial autocorrelation (PACF) functions had been envisioned to help in identifying appropriate orders of the Autoregressive model of order p (AR) and Moving Average model of order q (MA). The Bayesian Information Criteria (BIC) test was done on numerous models and it was found the model ARIMA (1, 1, 0) has the least BIC value. Ljung Q statistics data showed that there was no serial correlation across all the models. The standard error measures such as MSE, MAE, MAPE was calculated for the forecasting rates and was found to be minimal.

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Available abstract

In this paper ,Autocorrelation (ACF) and Partial autocorrelation (PACF) functions had been envisioned to help in identifying appropriate orders of the Autoregressive model of order p (AR) and Moving Average model of order q (MA). The Bayesian Information Criteria (BIC) test was done on numerous models and it was found the model ARIMA (1, 1, 0) has the least BIC value. Ljung Q statistics data showed that there was no serial correlation across all the models. The standard error measures such as MSE, MAE, MAPE was calculated for the forecasting rates and was found to be minimal.

Key concepts: Autocorrelation, Autoregressive integrated moving average, Partial autocorrelation function, Moving-average model, Autoregressive model, Statistics, Moving average, Bayesian information criterion

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