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In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, non-stationary, and time variance. Also, an appropriate feature selection is crucial for accurate forecasting. In this paper, a two-step approach that identifies a set of candidate features based on the data characteristics proposed and then selects a subset of them using correlation and instance-based feature selection methods, applied in a systematic way. Then, a combination of wavelet transform (WT) and a hybrid forecast method is presented based on neural network (NN) and an optimization algorithms. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods. These comparisons illustrate effectiveness of the proposed strategy.<\/jats:p>","DOI":"10.3233\/jifs-152073","type":"journal-article","created":{"date-parts":[[2017,3,7]],"date-time":"2017-03-07T10:20:26Z","timestamp":1488882026000},"page":"4031-4045","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":161,"title":["A new feature selection and hybrid forecast\u00a0engine for day-ahead price forecasting of\u00a0electricity markets"],"prefix":"10.1177","volume":"32","author":[{"given":"Abbas Rahimi","family":"Gollou","sequence":"first","affiliation":[{"name":"Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noradin","family":"Ghadimi","sequence":"additional","affiliation":[{"name":"Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2017,3,4]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"ShahidehpourM. 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