Abstract
Over the years, different researchers have followed one-step-ahead method for forecasting the time series data. However, one-step-ahead forecasting may not assist people for making better decision in problems, like sales data forecast, stock market forecast, weather forecasting, and so on. Therefore, for such type of problems multi-step-ahead forecasting will be a better solution for people to make the correct decision. In this current research, we used chemical reaction optimization (CRO) algorithm hybridized with pi-sigma neural network (PSNN) for multi-step-ahead forecasting of different time series data. In order to measure the accuracy of the proposed CRO-PSNN model, we compare the forecasting results with Jaya-PSNN and TLBO-PSNN using ten time series data sets. The proposed CRO-PSNN provided best result in six time series using RMSE measure and five time series using SMAPE measure. Moreover, in the Nemenyi hypothesis test, the proposed CRO-PSNN model has the best rank, which shows its significance.
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Pattanayak, R.M., Behera, H.S., Panigrahi, S. (2020). A Multi-step-Ahead Fuzzy Time Series Forecasting by Using Hybrid Chemical Reaction Optimization with Pi-Sigma Higher-Order Neural Network. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_88
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DOI: https://doi.org/10.1007/978-981-13-9042-5_88
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