# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
## 1 Introduction  
Audience attention is a factor that has the most significant impact on the pricing decision of Digital Signage Advertising (DSA) [1]. Generally, the higher the attention level received by a digital signage advertisement, the higher the profit expectancy. Various environmental factors can influence the audience attention level, including location popularity, temperature and air humidity, weather condition, social class of the surrounding community, and others [2]. These factors are important in determining the pricing decision for DSA to maximize revenue and increase advertising effectiveness. The current pricing solutions of DSA show some deficiencies in obtaining the optimal pricing decision. Commonly, the pricing mechanisms applied to DSA are the fixed-price strategy or the auction-based pricing strategy [1; 3]. In a fixed-price strategy, the advertising spot in a specific digital signage is charged with a fixed predetermined price. It can be in the form of direct sales or a guaranteed contract from the service provider. The price is commonly established based on the audience attention level of an advertisement spot. However, the changes of attention level toward the spot in time due to the changes in the surrounding environmental factors are not considered, resulting in sub-optimal pricing. In an auction-based pricing strategy, the price varies based on contextual information such as the location and time, the number of detected mobile devices nearby, and others. This information is used as the factors to generate a bid, and the advertising spot willbe sold to the highest bidders. This approach is beneficial in the sense of better utilizing the resources of the service provider. However, using such a scheme might be inefficient for the advertiser because it requires time-consuming bidding actions. Thus, dynamic pricing solutions need to create a significant benefit to the service provider as well as the advertiser [4]. With recent technology advancements, more dynamic pricing solutions are emerging, applying time-series forecasting methods to predict future activities based on historical data [5]. They provide a strong foundation for time-series forecasting dynamic pricing solutions using statistical, AI, and hybrid models [6; 7]. There are several ways to categorize the time-series forecasting models. [PERSON] suggested three main categories of time-series forecasting methods as statistical models, Artificial Intelligence (AI) models, and hybrid models [6]. On the other hand, [PERSON] and [PERSON] classified the time-series forecasting models as stochastic models, AI models, and regression models. However, both the regression and stochastic models are using similar statistical inferences to establish the relationship between the price and other relevant factors. Hence, both stochastic and regression models are classified under the subcategories of statistical models in our review. Figure 1 elucidates the classification tree of the seven most widely used time-series forecasting models on price estimation. The existing price forecasting methods that applied the time-series forecasting models are exemplified in different domains for its applicability. However, as a real-world problem is often complicated, no single model can perform the best in every situation [8]. With an increasing number of models, the model selection decision is also difficult, especially when vital information and knowledge are insufficient. Hence, a model selection framework is crucial for identifying a suitable approach for different data characteristics. From the review, there are four relevant essential data characteristics: namely, data linearity, stationarity, volatility, and the size of the dataset. These data characteristics are essential factors in selecting the optimal forecasting model for pricing decisions, particularly in its usage and interpretability in DSA.