## 3 Results
### Exhaustive Descriptions of Included Studies
#### 3.2.4 Group 4: Hybrid Model  
A Weight-adjusting Approach on an Ensemble of Classifiers for Time Series Forecasting [61]: is focused on forecasting time series using hybrid heterogeneous forecasting model including ARIMA model, SVM and ANN. The approach used in this paper is to take each model's weight based on their ability and history of predicting numerical values. The data used in this study were taken from the machine learning repository at UCI [62]. It included CO, relative humidity, Benzene concentration, etc., from March 2004 to April 2005. For this study from the air quality data set the hourly averaged CO was used. For evaluation purposes, MAE and MAPE were used. Comparing to each single classifier in the ensemble and with RF, the results showed that the proposed method had better performances (MAE-0.5779 and MAPE-30.52%) and also time complexity was O(N), where N is the size of validation data set. The experiments showed that after weight adjusting, the weight of SVM was always larger, which confirms that the role of SVM is more important. The weight of ARIMA was always the smallest, which raises doubts about the choice of ARIMA for time series prediction. Regarding future work, the authors mentioned the use of more than three classifiers and removing classifiers with negative weight. Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM\({}_{2.5}\) Concentration Forecasting: A Case Study of Beijing, China [63]: [PERSON] and [PERSON] proposed a hybrid model based on ESN and an Improved Particle Swarm Optimization (IPSO) to forecast PM\({}_{2.5}\) in Beijing city. First of all, the authors applied Phase Space Reconstruction to map the original data to the high- dimensional space, then Particle Swarm Optimization (PSO) for increasing the searching speed, and by taking into account the fact that PSO can face the problem to find the global minimum, the Convergence Cross-Mapping for proper subset selection was applied. Finally, ESN was applied for prediction. The dataset included hourly averages of PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), O\({}_{3}\), CO, temperature, pressure, humidity, wind speed, and wind direction from 1 January 2016, to 31 December 2016. The following prediction criteria were used for evaluation of the effectiveness of the proposed hybrid model, including RMSE, MAE, SMAPE, and R. The following models were selected for comparison purposes, such as the original model (ESN), Single-hidden Layer Feedforward Network, ELM, BPNN, LSSVM, and LSTM. The authors provided one-step and 10-step forecasting experiments. The results for both steps showed that the proposed model provided better performances among all models. The limitation was that it failed to consider the potential factors in extreme conditions (e.g., radon emissions). An additional extension can be to achieve medium- and long-term forecasts in terms of the time factor. _PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors [64]:_ is focused on forecasting of next 30 days' PM2.5. The proposed model, CEEMD-PSOGSA-SVR-GRNN, is based on Complementary Ensemble Empirical Mode Decomposition (CEEMD), Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), SVR, Generalized Regression Neural Network (GRNN) and Grey Correlation Analysis (GCA). The data were collected from Chongqing, Harbin and Jinan in China from 5 December 2013 to 20 August 2015. For evaluating the following metrics were used: MAE, MAPE, RMSE, R, and IA. The results showed that the suggested hybrid model had relatively better performances. As future work, the authors proposed to apply the method to forecast other air pollution indexes and to evaluate the air quality in other cities.