This paper introduces a new class of Bayesian dynamic models for inference and forecasting in high-dimensional time series observed on networks. The new model, called the dynamic chain graph model, is suitable for multivariate time series which exhibit symmetries within subsets of series and a causal drive mechanism between these subsets. The model can accommodate high-dimensional, non-linear a...

This paper proposes a novel forecasting method which is applicable for new services, where little historical data has been recorded. The method uses instead estimators based on economical, demographic and traffic data. The method is, compared to traditional forecasting procedures that are built upon a solid historical record of data, clearly found to be weaker numerically. However, for novel se...

Autoregressive integrated moving average (ARIMA) models are used in different researches for modelling and forecasting of traffic and Quality of Service (QoS) parameter values in telecommunication networks to make reasonable short, mediumand long-term predictions. We propose methodology to use ARIMA models for QoS prediction in network scenarios based on a preliminary detection and elimination ...

Combining the ant colony algorithm (ACA) and the neural network (NN), the present paper puts forward an approach to traffic volume forecasting based on the ant colony neural network. The approach employs the ACA with mutation features to train the weights of an artificial neural network (ANN), thus it is characterized by large mapping capacity of the NN, and by rapidity, global convergence, and...

We present Tensor-RNN, a novel RNN architecture for multivariate forecasting in chaotic dynamical systems. Our proposed architecture captures highly nonlinear dynamic behavior by using high-order Markov states and transition functions. Furthermore, we decompose the highdimensional structure of the model using tensortrain networks to reduce the number of parameters while preserving the model per...

Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture dependencies. However, most use a single predefined matrix or self-generated matrix. It is difficult obtain deeper spatial information by only relying on adjacency In this paper, we present progressive multi-graph convolutional ...