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Internet traffic volume estimation has a significant impact on the businesspolicies of the ISP (Internet Service Provider) industry and businesssuccessions. Forecasting the internet traffic demand helps to shed light on thefuture traffic trend, which is often helpful for ISPs decision-making innetwork planning activities and investments. Besides, the capability tounderstand future trend contributes to managing regular and long-termoperations. This study aims to predict the network traffic volume demand usingdeep sequence methods that incorporate Empirical Mode Decomposition (EMD) basednoise reduction, Empirical rule based outlier detection, and $K$-NearestNeighbour (KNN) based outlier mitigation. In contrast to the former studies,the proposed model does not rely on a particular EMD decomposed componentcalled Intrinsic Mode Function (IMF) for signal denoising. In our proposedtraffic prediction model, we used an average of all IMFs components for signaldenoising. Moreover, the abnormal data points are replaced by $K$ nearest datapoints average, and the value for $K$ has been optimized based on the KNNregressor prediction error measured in Root Mean Squared Error (RMSE). Finally,we selected the best time-lagged feature subset for our prediction model basedon AutoRegressive Integrated Moving Average (ARIMA) and Akaike InformationCriterion (AIC) value. Our experiments are conducted on real-world internettraffic datasets from industry, and the proposed method is compared withvarious traditional deep sequence baseline models. Our results show that theproposed EMD-KNN integrated prediction models outperform comparative models.

DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction