# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
## 2 Review on Time-Series Forecasting Studies
### Artificial Neural Network (ANN)  
[PERSON] and [PERSON] proposed the ANN in 1943 [115], which is a computational model inspired by the human biological nerve system. It is composed of layers of connected neurons. ANN has been applied in many domains for time-series forecasting, including the forecasting of prices for the agricultural product [62], electricity [63; 64; 65; 66], gold [67], and stock [68; 69; 70] from 2004 to 2020. The application of ANN is more robust than the statistical models in capturing the trends and seasonality of the data--the non-stationarity of the data [62]. ANN is proven to work better than the statistical model with the time-series data that are nonlinear, stationary, and have sudden changes through the layering hierarchy [70]. Despite its superior result, the one significant drawback of ANN is its black-box nature [116]. The computational process of ANN is complex, and how the model decides is hardly understandable. There is also the risk of overfitting when there are too few data observations. The model will only memorize the unique data features, which means that it does not learn the underlying relationship of the data, resulting in lower generalizability. Table 7 summarizes the studies of ANN time-series forecasting. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Domain** & **Author** & **Dataset** & **Model** & **Result** \\ \hline \multirow{4}{*}{**Gold**} & \multirow{4}{*}{[PERSON] and [PERSON], 2017 [60]} & \multirow{4}{*}{DJIA, S\&P500, USDX} & RF &
\begin{tabular}{l} RF showed a promising \\ result in predicting the \\ different datasets, with \\ prediction performance \\ up to 0.99 \\ \end{tabular} \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Domain** & **Author** & **Dataset** & **Model** & **Result** \\ \hline \multirow{4}{*}{**Agricultural**} & \multirow{4}{*}{[PERSON] and [PERSON], 2013 [62]} & \multirow{4}{*}{\
\begin{tabular}{l} Soybean price from SOPA, \\ rapeseed-mustard from Delhi \\ \end{tabular} } & \multirow{4}{*}{ANN, ARIMA, \\  & & & & \\  & & & & TDNN \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{4}{*}{**Electricity**} & \multirow{4}{*}{[PERSON] et al., 2004 [63]} & \multirow{4}{*}{
\begin{tabular}{l} Californian power market \\ (January 1999 to September \\ \end{tabular} } \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: ANN time-series forecasting from 2004 to 2020. Recurrent Neural Network (RNN)  
RNN is a variation of ANN [117] with a feedback architecture that contains a feedback loop for later layers of the network to go back to the input layer. RNN has been proven to be a suitable method for time-series forecasting, as exemplified by the studies in forecasting the prices of stock [71; 72; 73; 74; 75], Bitcoin [76], fuel [77], gold [78], electricity [79; 80; 81], and agricultural [82] from 2006 to 2020. The introduced hidden state in RNN with the ability to memorize previous information helps improve its forecasting capability, even though the data scale is more extensive [82]. Long short-term Memory (LSTM) is introduced also as a refinement of RNN in remembering values for longer or shorter times [72]. From the reviews, RNN has shown an excellent result in nonlinear time-series forecasting as compared to the statistical model. RNN can model both the nonlinear and high volatile data well. Therefore, RNN would be a good choice for time-series forecasting for the above-mentioned data characteristic, provided that there are sufficient data observations. Table 8 shows the summarized review on the studies of RNN for time-series forecasting.