Machine learning has been widely applied in structural health monitoring. While most existing methods, which are limited to forecasting state evolution of large infrastructures. forecast the a step-by-step manner, extracting feature trends and negative effects data collection under abnormal conditions big challenges. To address these issues, long-term trend method based on long sequence time-se...

This paper proposes a structure for long-term energy demand forecasting. The proposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF) model as the forecaster and utilizes the Hodrick–Prescott (HP) filter for extraction of the trend and cyclic components of the energy demand series. Besides, the sophisticated technique of mutual information (MI) is employed to select the...

Air cargo loading weight forecasting is an important factor for managers in the aviation industry because revenue is dependent on the amount of weight loaded. In this paper, we propose a new method to improve forecasting accuracy and confirm them by the numerical example. Focusing that the equation of exponential smoothing method(ESM) is equivalent to (1,1) order ARMA model equation, a new meth...

203 words) In ITS (Intelligent Transportation System) equipped urban transportation systems noncritical junctions are often ignored in short-term traffic condition prediction algorithms as the traffic data collection systems in these junctions are not adequate. The paper proposes a shortterm traffic volume model based on a combination of discrete wavelet transform (DWT) and Bayesian hierarchica...

Hemorrhagic fever with renal syndrome (HFRS) is one of the most common infectious diseases globally. With the most reported cases in the world, the epidemic characteristics are still remained unclear in China. This paper utilized the seasonal-trend decomposition (STL) method to analyze the periodicity and seasonality of the HFRS data, and used the exponential smoothing model (ETS) model to pred...

Applications of exponential smoothing to forecast time series usually rely on three basic methods: simple exponential smoothing, trend corrected exponential smoothing and a seasonal variation thereof. A common approach to select the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute percentag...