Self-Identification ResNet-ARIMA Forecasting Model (original) (raw)
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Autoregressive integrated moving average (ARIMA) models have been proven successful in application and simple in comprehension and consequently, they have been widely applied to different fields in forecasting. The order of an ARIMA model is determined subjectively based on the judgment of the experts where, the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots for a given time series are used to determine the potential orders of the model. In this paper, a new heuristic algorithm is proposed for determining the order of ARIMA models. The proposed method determines the order of the ARIMA models, objectively, based on the Mean Squared Error (MSE), Akaike Information Criterion (AIC), and Schwarz Bayesian Information Criterion (BIC). In this regard, the order of the models is determined objectively and as a result, the forecasting results would be more accurate. The performance of the proposed method is evaluated based on a real-world dataset of global temperature anomaly where, the results show that the proposed method performs accurately and efficiently in determining the order of ARIMA models.
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Proc. of World Congress of Engineers, London, 2007
For the analysis and forecasting of time series, we always search for a statistical model capable of understanding the underlying processes of data and filtering the unwanted noise. This noise may either be white or coloured, having some pattern of autoregressive moving average i.e., ARMA(p,q) processes. This search is carried out by using various forecast accuracy criteria and tools such as, Akaike's information criterion (AIC) and Akram test statistic (ATS). As compared to others, the AIC and ATS are noted to be more effective for identification of good models. However, AIC, being parametric in nature, is found to be comparatively more sensitive to noise volatilities and cumbersome to use; whereas, ATS, the base of which is distribution free is observed to be quite robust to noise variations, parsimonious in nature and relatively more easy to use. In this paper both the AIC and ATS are reviewed, practical implication discussed and their role in identifying optimum models from a class of candidate statistical models, especially, the linear dynamic system models is examined. For better insight, into these gadgets an example on analysis and forecasting of daily copper prices is given.
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