A Hybrid ARIMA and Artificial Neural Networks (ANN) Model for Daily Maximum and Minimum Temperature Forecast
Abstract
Prediction of the daily maximum and minimum temperatures is one of the most important topics in climate research and all vital areas. It can help managements in the economic and strategic planning and decision-making.
This research use time series single models of linear Box- Jenkins ARIMA (p,d,q) and non-linear ANN models and then integrates them into the ARIMA-ANN model, assuming series includes linear component estimated using a Box- Jenkins (ARIMA) model, and a non-linear component represented by model errors (Residual) can be estimated using ANN models to obtain the best and most accurate future prediction for time series.
The selected model to present daily minimum temperature serie In Al-Basel Station including linear model ARIMA (1,1,1) and non- linear model ANN (1-26-1); while model ARIMA (0,1,2) and model ANN (1-17-1) were selected to present daily maxmum temperature serie, The results showed that (ARIMA-ANN) model was superior to other models according criteria of accuracy check (R, RMSE, MAE, MAPE).
التنبؤ بدرجات الحرارة العظمى والصغرى من الموضوعات الهامة في المناخ لكافة المجالات الحيوية، إذ يساعد الإدارات في التخطيط الاقتصادي والاستراتيجي واتخاذ القرارات المستقبلية.
تناول هذا البحث دراسة لاستخدام نماذج مفردة من: نماذج بوكس-جينكنز ARIMA (p,d,q) الخطية، ونماذج الشبكات العصبية الاصطناعية (ANN) اللاخطية، ومن ثم الدمج بينهما في نموذج (ARIMA-ANN)، على فرض أن السلسلة تضم مركبة خطية يتم تقديرها باستخدام نموذج (ARIMA)، ومركبة غير خطية ممثلة بأخطاء النموذج (Residual) يمكن تقديرها باستخدام نماذج (ANN)، وذلك للحصول على أفضل وأدق التنبؤات المستقبلية للسلاسل الزمنية.
النموذج المُختار لتمثيل درجة الحرارة الصغرى في محطة سد الباسل يتضمن نموذج خطي ARIMA (1,1,1) ونموذج غير خطي ANN(1-26-1)، بينما اختير نموذج ARIMA (0,1,2)، ونموذج ANN(1-17-1) لتمثيل درجة الحرارة العظمى، وأظهرت النتائج تفوق نموذج (ARIMA-ANN) على النماذج الأخرى وفق معايير ضبط الدقة ((R, RMSE, MAE, MAPE.
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