This paper proposes an effective fusion of neural networks and grey modeling for adaptive electricity load forecasting. The fusion employs the complementary strength of these two appealing techniques. In terms of forecasting accuracy, the proposed fusion scheme outperforms the individual ones and the statistical autoregressive methods according to the results of a substantial number of experiments. In addition to the fusion scheme, this paper also proposes a grey relational analysis to automatically assess the importance of each input variable for the forecasting task. This analysis helps the forecaster choose dominant ones among the many input variables, thus removing much burden of acquiring professional domain knowledge for problems and reducing the interference of irrelevant inputs on the forecasting. Experimental results are shown in this paper to verify the effectiveness of the grey relational analysis.

Since the past decades, prediction of stock price has been an important and challenging task to yield the most significant profit for a company. In the era of big data, predicting the stock price using machine learning has become popular among the financial analysts since the accuracy of the prediction can be improved using these techniques. In this paper, auto-regressive integrated moving average (ARIMA), neural network (NN) and long short-term memory network (LSTM) have been used to predict Bursa Malaysia’s closing prices data from 2/1/2020 to 19/1/2021. All the models will be evaluated using root mean square errors (RMSE) and mean absolute percentage errors (MAPE). The results showed that LSTM able to generate more than 90% of accuracy in predicting stock prices during this pandemic period.