Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Conventional GM(1, 1|α) prediction always produces the huge singleton residual error around the turning point region of a time series and this phenomenon is called overshooting. A novel forecasting technique using a hybrid BPNN-ighted Grey-CLSP (BWGC) prediction that employs a back-propagation neural net (BPNN) to automatically adjust a linear combination of GM(1, 1|α) prediction and the cumulated 3-point least squared linear prediction (C3LPS) is presented herein to resolve this overshooting problem. This is because utilizing an underestimated output from C3LPS to offset an overshoot predicted output from the grey prediction will dramatically reduce the big residual error. This model exhibits a smoothing effect on the forecast to yield better an accuracy for the non-periodic short-term prediction. A three-layer BPNN with a structure of 5×14×2 multilayer-perceptron is used to tune the ights for both models. This approach was verified to be not only suitable for a stochastic type prediction (international stock price indices forecasting) but also for an inertia type prediction (forecasting the path of a typhoon).
['Bao Rong Chang']
I am writing a research paper on a idea Hybrid BPNN-Weighted Grey-CLSP Forecasting
[{"from": "human", "value": "I am writing a research paper on a idea Hybrid BPNN-Weighted Grey-CLSP Forecasting"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Conventional GM(1, 1|\u03b1) prediction always produces the huge singleton residual error around the turning point region of a time series and this phenomenon is called overshooting. A novel forecasting technique using a hybrid BPNN-ighted Grey-CLSP (BWGC) prediction that employs a back-propagation neural net (BPNN) to automatically adjust a linear combination of GM(1, 1|\u03b1) prediction and the cumulated 3-point least squared linear prediction (C3LPS) is presented herein to resolve this overshooting problem. This is because utilizing an underestimated output from C3LPS to offset an overshoot predicted output from the grey prediction will dramatically reduce the big residual error. This model exhibits a smoothing effect on the forecast to yield better an accuracy for the non-periodic short-term prediction. A three-layer BPNN with a structure of 5\u00d714\u00d72 multilayer-perceptron is used to tune the ights for both models. This approach was verified to be not only suitable for a stochastic type prediction (international stock price indices forecasting) but also for an inertia type prediction (forecasting the path of a typhoon)."}]