# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
## 2 Review on Time-Series Forecasting Studies
### Artificial Intelligence Model (AI)
#### 2.2.1 Machine Learning Model (ML)  
[PERSON] first proposed Support Vector Machine (SVM) in 1995 [108]. SVM is a type of learning algorithm proposed to improve the neural network's generalizability to achieve the global optimum solutions. SVM was first introduced for classification tasks, and it is extended for regression and time-series forecasting problems with excellent outcomes [47; 109]. SVM has been applied in various domains for price forecasting, such as crude oil [44; 45; 46; 47], rubber [48], gold [49], and electric [50; 51; 52; 53], agricultural [54], and stock [55] from 2005 to 2021. SVM can avoid the over-fitting problem and model the nonlinear relationships stably, as it applied the risk minimization principle in training. SVM has been tested and compared to statistical models such as ARIMA and has shown a better performance [47; 49]. The interpreted result suggests that SVM performs better for capturing nonlinear relationships and in handling irregularities than statistical models. However, SVM has lower transparency with very high dimensionalities, where the score of the results cannot be represented as a simple parametric function [110]. The model also requires many steps to get to the best parameters to fit the model. Nevertheless, SVM generally will not work well on a large dataset, as it is constrained by time and memory optimization [111]. From the review, the SVM is robust in modeling the nonlinear data. Table 5 shows the studies on the SVM models. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Domain** & **Author** & **Dataset** & **Model** & **Result** \\ \hline \multirow{8}{*}{**Crude oil**} & [PERSON] et al., 2006 [44] & WTI crude oil price (January 1970 to December 2003) & SVM, ARIMA, BPNN & SVM has the best performance with RMSE of 2.1921 \\ \cline{2-5}  & [PERSON] and [PERSON], 2009 [45] & OPEC, DJAIS and AMEX oil index & SVM & Error rate of 16.23\% \\ \cline{2-5}  & [PERSON] and [PERSON], 2011 [46] & WTI crude oil price (2002 to 2008) & SVM, ANN & SVM has the highest correct prediction rate of 81.27 \\ \cline{2-5}  & & SVM, ARIMA, FNN, ARIMA, MS-ARFIMA, Random walk, SVM & SVM has the best performance with highest \\ \cline{2-5}  & [PERSON] et al., 2017 [47] & WTI crude oil price & SVM & Diebold–Mariano test score \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SVM time-series forecasting from 2005 to 2021. Random Forest (RF) is an ensemble method formed by many decision trees that are used for both classification and regression processes, which was introduced by [PERSON] in 2001 [112]. RF has been applied in time-series forecasting for dynamic pricing, including forecasting the prices of power load [56] and electricity [57], diamond [58], exchange rate [59], gold [60], and stock [61] from 2006 to 2021. RF works better for data with high volatility and randomness, such as exchange rate, and it has outperformed the SVM [59]. In addition, RF can work faster and better on large training samples, with the training time significantly faster than the SVM. However, some studies have shown that RF performed worse than the regression models in modeling linear data [55; 113]. In short, it is suitable for nonlinear, stationary, and highly volatile data. Table 6 included the studies of the RF models.