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Time Series Forecasting of Crude Oil Consumption Using Neuro-Fuzzy Inference

Shaya Rubinstein, Aaron Goor, and Alexander Rotshtein
Dept. of Industrial Engineering & Management Jerusalem College of Technology, Jerusalem, Israel
Abstract—Forecasting time series with lengthy and chaotic history can be challenging and complex. The demand of crude oil in the U.S. from 1974 – 2012 has much chaotic behavior. Many statistical methods are available for time series modeling for forecasting, however choosing the right method or methods is a difficult task. Forecasting and prediction models based on adaptive neuro-fuzzy inference systems (ANFIS) have shown to predict satisfactory error ranges in multiple fields of study. By experimenting with ANFIS we have succeeded to develop a method for modeling time series data parameters: the embedded delay and number of input variables. Our results show that ANFIS behavior across data models is intuitive and its projected forecast errors are indeed satisfactory. In addition, our best 12-month delay model in ANFIS provided a 12-month-ahead forecast with strikingly similar seasonal behavior to a forecast provided by the U.S. Dept. of Energy – Energy Information Administration for the same time period, and resulted in a lower overall projected forecast error. ANFIS as a parameter modeling tool for unaltered time series is therefore suggested to be quite helpful.

Index Terms—ANFIS, crude oil, forecasting, fuzzy logic, neural networks

Cite: Shaya Rubinstein, Aaron Goor, and Alexander Rotshtein, "Time Series Forecasting of Crude Oil Consumption Using Neuro-Fuzzy Inference," Journal of Industrial and Intelligent Information, Vol. 3, No. 2, pp. 84-90, June 2015. doi: 10.12720/jiii.3.2.84-90
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