negative polarity are created by the decision tree. Classifying sentiments of one English document is identified based on the association rules of the positive polarity and the negative polarity. Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews. We have tested our new model on our testing data set and we have achieved 60.3% accuracy of sentiment classification on this English testing data set.
Time series forecasting has many practical applications in a variety of domains such as commerce, finance, medicine, weather, environment, and transportation. There exist so many methods developed for time series forecasting. However, most of the forecasting methods do not pay attention to anomalies in time series even though time series are sensitive to anomalies. Anomaly patterns cause negative effects on the accuracy of time series forecasting. In this paper, we propose a novel anomaly repair-based approach to improve time series forecasting in the case of anomaly existence. In our approach, an effective time series forecasting framework, EPL_S_X, is proposed with anomaly smoothing as a pre-processing stage and any existing time series prediction algorithm X. In particular, our proposed approach consists of three steps including detecting anomalies, repairing anomalies by using our smoothing method, and forecasting time series using preprocessed time series. Experimental results on several time series datasets reveal that our proposed approach improves remarkably the accuracy of many existing time series forecasting methods. It also outperforms the two robust time series forecasting methods that are based on exponential and Holt-Winters smoothing. With such better prediction performance, our approach is not only more effective but also more useful when dealing with anomalies in time series forecasting.
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