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As the conventional multistep‐ahead prediction may be unsuitable in some cases, the purpose of this paper is to propose a novel method based on joint probability distributions, which provides the most probable estimation for the predicted trajectory.

Many real‐time series can be modeled in hidden Markov models. In order to predict these time series online, sequential Monte Carlo (SMC) method is applied for joint multistep‐ahead prediction.

The data of monthly national air passengers in China are analyzed, and the experimental results demonstrate that the method proposed and the corresponding online algorithms are effective.

In this paper, SMC method is applied for joint multistep‐ahead prediction. However, with the increasing of prediction step, the number of particles is increasing exponentially, which means that the prediction steps cannot be too large.

A very useful advice for researchers who study time‐series forecasts.

A novel method of multistep‐ahead prediction based on joint probability distribution is proposed and SMC method is applied to prediction time series online. This paper is aimed at those researchers who focus on time‐series forecasts.

The purpose of this paper is to improve the accuracy of short time load forecasting to ensure the economical and safe operation of power systems. The traditional neural network applied in time series like load forecasting, easily plunges into local optimum and has a complicated learning process, leading to relatively slow calculating speed. On the basis of existing literature, the authors carried out studies in an effort to optimize a new recurrent neural network by wavelet analysis to solve the previous problems.

The main technique the authors applied is referred to as echo state network (ESN). Detailed information has been acquired by the authors using wavelet analysis. After obtaining more information from original time series, different reservoirs can be built for each subsequence. The proposed method is tested by using hourly electricity load data from a southern city in China. In addition, some traditional methods are also applied for the same task, as contrast.

The experiment has led the authors to believe that the optimized model is encouraging and performs better. Compared with standard ESN, BP network and SVM, the experimental results indicate that WS‐ESN improves the prediction accuracy and has less computing consumption.

The paper develops a new method for short time load forecasting. Wavelet decomposition is employed to pre‐process the original load data. The approximate part associated with low frequencies and several detailed parts associated with high frequencies components give expression to different information from original data. According to this, suitable ESN is chosen for each sub‐sequence, respectively. Therefore, the model combining the advantages of both ESN and wavelet analysis improves the result for short time load forecasting, and can be applied to other time series problem.

This paper aims to explore the role of digital inclusive finance (DIF) in influencing household tourism consumption, whether this influence differs between households with different characteristics and determining the intermediate mechanisms that influence the relationship.

The conceptual framework of this study was designed on the basis of the research on DIF in residential consumption practices. The China Household Finance Survey (CHFS) and the Peking University DIF Index were used in the study, which included four years of unbalanced panel data from 25 provinces in China. A fixed effects model was used to validate the conceptual framework and hypothesis testing.

Both hypothesis paths proposed in this study were supported. Results of this study show that DIF has a significant contribution to household tourism consumption and shows a positive impact in terms of both breadth of coverage and depth of use, and that Internet usage is an important mediating mechanism for DIF to promote household tourism consumption. Thus, the use of DIF as a tool can have a positive impact on tourism consumption.

Results of this study will help researchers and tourism businesses understand the relationship and mechanisms at play between DIF and household tourism consumption and leverage financial tools to drive tourism revival. However, the lack of third-country data for comparative analysis may render the conclusions inapplicable to every economy.

This study is the first to examine the relationship between DIF and household tourism consumption, using an “individual + time + region” fixed effects model to conduct specific empirical tests.