## 2 Literature Review  
Hybrid models typically combine prediction algorithms with the data decomposition techniques and give full play to the advantages of each component to generate more accurate predictions. As an extension of SVMs, SVR was proposed by [PERSON] et al. [22] for regression analysis possessing superior capability in solving multidimensional, nonlinear problems with small samples [6], and has been proved to be effective in solving various forecasting problems [23; 24]. Therefore, SVR-based models, which combine the SVR model with data decomposition techniques, are extensively used in time series prediction [25; 26]. The EWT, which is a novel tool for data decomposition, has been successfully applied in many research areas [27; 28; 29] and was chosen as a data decomposition technique in this study to preprocess the original grouiting power series, by which the original grouting power series is decomposed into a specific number of subseries and one residual series adaptively. When employing SVR models for the decomposed subseries prediction, the prediction performance relies greatly on the input variables and hyperparameters of forecasting components. Hence, identifying optimal input variables and hyperparameters is particularly necessary. The PACF is used to determine the optimal input variables by calculating the correlation between data [30; 31; 32]. Meanwhile, identifying the optimal model hyperparameters is an optimization problem. Many studies use optimization algorithms to solve such optimization problems [33]. One of these methods employs the Java algorithm, which is a new population-based optimization method [34]. The main advantage of this algorithm is the fact that only two common parameters (e.g., the number of generations and the statistical population size) are required and no other specific parameters should be tuned. Unlike the Java algorithm, some other meta-heuristic algorithms require algorithm-specific parameters except for the two common parameters. For example, the DE algorithm needs the scaling factor and crossover probability, and the genetic algorithm requires mutation probability, crossover probability, and a selection operator. In addition, the Java algorithm only contains one update equation. Because of the above properties, this algorithm is easy to implement. Therefore, the Java algorithm has been extensively used in various practical optimization problems such as product design [35], parameter estimation [36], and image identification [37]. Although the Java algorithm has been proven to perform well, it still has the problem of premature convergence and becoming trapped in local optima frequently [38]. To overcome these problems, a new version of the Java algorithm is proposed herein. First, tent chaotic mapping has been employed in the Java algorithm to initialize the statistical population and preserve statistical population diversity. Moreover, we incorporate Levy flights into the statistical population updating phase to enhance search capabilities and increase the probability of finding the optimal solution. This improved Java (IJaya) algorithm is then used to find the optimal hyperparameters of the SVR model. Inspired by the concept of hybrid models, a novel hybrid model incorporating the EWT, PACF, SVR and IJaya algorithm is proposed for grouting power prediction based on \"decomposition-prediction -integration\". The main aim of decomposition is to simplify the modelling complexity through decomposing the complex grouting power series into relatively simple subseries and one residual series, and denoise the original time series by discarding the residual series. Then the purpose of prediction is to decrease the prediction errors by using appropriate forecasting methods. Finally,the purpose of integration is to yield the final result for the original series by aggregating prediction results of each subseries. The principal contributions of this paper can be summarized as follows:  
1.