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The purpose of this paper is to present research in the area of the applications of the generalized inverse matrix in IP traffic matrix.

Traffic matrices are important for many network design, engineering, and management functions. However, they are often difficult to measure directly. Because networks are dynamic, analysis tools must be adaptive and computationally lightweight. In order to manage the whole network, a novel calculating model is proposed based on the generalized inverse matrix. In this model, a generalized inverse matrix is introduced to resolve the traffic matrix equation. But if so, the error is raised. In order to improve the method, an original traffic matrix is estimated according to the prior, for example, Poisson model. To acquire the optimized solutions, linear programming is introduced. Through both theoretical analysis and simulating results, it is shown that the proposed algorithm achieves better performance than the existing representative methods.

This paper illustrates the useful information that can be obtained using generalized inverse matrix for incomplete data estimation.

The use of generalized inverse matrix was a very effective method to calculate IP traffic matrix.

The algorithms discussed in the paper can be used to estimate solutions of an ill‐posed linear inverse equation.

The paper is of value in proposing an estimation method for IP traffic matrix using generalized inverse matrix.

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.