Predictive Models for Art Festival Planning

 

Eeshita Goyal ¹, Nidhi Sharma ², Santosh Kumar Behera ³, Dr. Kruti Sutaria ⁴, Ramesh Saini ⁵, Devanand Choudhary ⁶

 

¹ Assistant Professor, School of Business Management, Noida International University, Greater Noida, Uttar Pradesh, India

² Associate Professor, Department of Development Studies, Vivekananda Global University, Jaipur, India

³ Assistant Professor, Department of Centre for Artificial Intelligence and Machine Learning, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

⁴ Assistant Professor, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India

⁵ Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India

⁶ Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037 India

 

1. INTRODUCTION

Art festivals are diverse, exciting cultural ecosystems that have been associated with creativity, community involvement, tourism and local economic development. These, such as performing arts festivals and visual exhibitions, music and literary gatherings, are important forums of cultural expression and socio-economic stimulation. Nevertheless, the process of organizing an art festival is complicated by planning, distribution of resources, and preemptive decision making. Such variables as the number of people attending the audience, the demand of the tickets, dynamics of sponsorship, weather, and the logistics have to be predicted successfully. The old forms of planning based on experience or some fixed past averages are not very good at reflecting the dynamic and multidimensional nature of festivals today. Thus, predictive models and data-driven analytics integration into the sphere of managing festivals are becoming a radically new trend to improve efficiency, sustainability, as well as audience engagement. Predictive modeling is based on statistical and computational methods to derive actionable insights out of data so that organizers can prevent the occurrence of trends, allocate budgets suitably, and reduce risks even before they take place Siri (2024).

Machines learning (ML) mixed with time-series forecasting and deep learning (DL) permit predicting such important indicators as attendance, sales, operational costs, and environmental impact with high precision. An example of these is time-series based models such as ARIMA and Prophet that offer seasonal forecasting, and multi-layered ML models such as XGBoost and Random Forest that learn more complicated non-linear relationships between variables of interest. In addition, architectures of the deep learning models such as Long Short-Term Memory (LSTM) and Transformer-based predictors have shown impressive results in predicting sequential dependencies in social media trends, ticketing trends, and meteorological information Mossavar-Rahmani and Zohuri (2024). The success of art festivals is based on the match of forecasting demand with resources provisioning, and therefore forecasting is always required. Specifically, by comparing data collected through multiple sources, such as ticketing systems and social media analytics, as well as tourism flows and weather forecasts, organizers will be able to anticipate attendance spikes, plan performances strategically, and arrange venue designs to control the number of people and organizational logistics. Moreover, the ability to predict the changes in the budgets and marketing effects enables the administrators to adjust promotion campaigns and sponsorship plans in-progress Qin et al. (2023). This predictive intelligence is able not only to increase the profitability but also to contribute to the cultural sustainability as it contributes to the evidence-based decision-making throughout all stages of the event lifecycle, i.e., pre-festival planning, live operations, and post-event evaluation.

 

2. Literature Review

2.1. Overview of predictive analytics in event planning

The general utilization of the past (historical data), statistical techniques and computational model to predict the future is known as predictive analytics, which has become quite popular in the process of event planning. When applied in the context of events (conferences, festivals, concerts, cultural gatherings), predictive analytics can assist in changing the intuition-driven decision-making of planning by switching to data-driven planning. Organizers use historical ticket purchase statistics, attendance data, demographics, and contextual factors (e.g., seasonality, weather, whenmarketing, etc.) to predict attendance, allocate resources, change marketing campaigns, and optimize total logistics Singh et al. (2023). Research in the events sector indicates that data analytics are applied to learn the consumer behaviour, enhance marketing approaches and inform economic decision-making through pricing, staffing and resource allocation. Predictive analytics can help to better use the available resources, reduce risks, and enhance the experience of attendees by converting vast amounts of past and current data into insights and actions (better scheduling, crowd management, and service provisioning) Huang and Rust (2020).

 

2.2. Machine learning applications in cultural and tourism forecasting

The field of tourism and cultural affairs has experienced a swift increase in the number of machine learning (ML) and other AI-based methods of demand, visitor numbers, and cultural attendance forecast. The review of the literature shows that supervised learning methods, such as neural networks, decision trees, ensemble methods, are becoming more popular than the traditional econometric/time-series methods, as they can model non-linear relationships, accommodate non-dimensional data, and combine various features, such as economic variables, search-engine query data, and social media signals Huang et al. (2025). Indicatively, there are literature studies in this area that have come up with ML-based tourism demand forecasting frameworks which perform better than classical linear or time-series frameworks using deep neural networks or hybrid frameworks. ML classifiers such as k-NN, decision trees, or Naive Bayes have been applied to species such as visitor categories (ex: high, medium, low visitation) in environmental- or location-sensitive environments such as protected natural areas allowing improved conservation planning and resource allocation Villaespesa and Murphy (2021). More recently, long-range, temporal forecasting of tourism demand has been investigated using deep learning and state-of-the-art sequence-modeling techniques, such as Long Short-Term Memory (LSTM) networks or by using Transformer-related models. These techniques have the ability to represent complicated time-dependencies, seasonality, and time-varying patterns compared to the conventional time-series models Cetinic and She (2021).

 

2.3. Gaps and limitations in existing research

Regardless of such promising developments, the current body of literature covering the predictive analytics, ML, and deep learning approaches in event, tourism, and cultural forecasting has a number of noteworthy gaps and limitations. First, a number of event-industry research is conceptual or descriptive in nature; frameworks of systematic end-to-end prediction (data collection to deployment) are still infrequent. In the case of festival-type events, where there exists complex dynamics, heterogeneous crowds, and contexts that are not identical, empirical research that effectively models attendance, demand, budget and logistics at the same time is limited Rani et al. (2023). Second, although the ML and deep learning models have yielded better predictive results, they are prone to be affected by the so-called black-box problems. Explainability and interpretability are also poor in most models especially deep neural networks, and as such, the application of these models in stakeholders that require clear decisions is hampered. Third, there is a significant challenge of data availability and quality Avlonitou and Papadaki (2025). The Table 1 summarizes the existing predictive analytics research in event and festival planning, including how it is done, what it contributes, and what it does not reveal. Numerous forecasting works use aggregate macro-data (e.g., arrival of tourists, search results, economic indicators) instead of more event-specific data (ticket sales, demographics of attendees, social media sentiment, weather, local mobility).

Table 1

Table 1 Comparative Review of Related Work on Predictive Analytics and Machine Learning Models for Event and Festival Planning
Domain	Features Used	Methods	Target	Limitations
Event-based Social Networks (EBSN) Cao et al.  (2023)	User event history; user and event context data; social / network features	Context-aware ML / recommendation-style predictive algorithms	Predicting number of participants / user attendance	Focus on small/online-based events; less on large-scale festivals, logistics or budget planning
Theme-park / tourist-attraction attendance forecasting	Five years of historical park visitation data + external variables	Machine-learning models (gradient-boosting)	Forecasting daily/periodic attendance	Focused on theme-park context; may not generalize to diverse festival types with more dynamic external factors
Tourism demand forecasting and analysis Maerten and Soydaner (2023)	Aggregated tourism and travel data, economic and search/social proxies, seasonal factors, external variables	Diverse ML / DL / ensemble / statistical methods	Forecasting tourism demand, predicting tourist flows/visitation	General tourism context; may lack festival-specific features (ticket sales, event-type, social-media buzz)
Tourism — cave / natural-site visitation forecasting	Historical visits, external variables (e.g. Google-Trend data)	Hybrid ARIMA + neural network (“NeuralProphet-like”)	Forecasting daily/seasonal visitor demand	Context: static attraction site; festivals have more dynamic scheduling, promotional and logistic variables
Cultural venues / event attendance and forecasting	Footfall (visitor count), time data, possibly social/media/engagement data	Neural-network based forecasting models	Forecasting visitor counts / footfall for cultural venues	May not include budget or resource allocation forecasting; less focus on multi-variable predictive frameworks (logistics, budgets)
Urban tourism and cross-city flows for public events Oksanen et al. (2023)	Social media data, event metadata, mobility/travel data	ML models incorporating LLM-based feature extraction + flow prediction models	Predicting visitor flows to events across cities	Focus on visitor flows/transport rather than on budget or internal festival logistics; methodology may need adaptation for smaller-scale festivals
Festival production and management (industry case) Tang et al. (2021)	Historical ticket sales, booking trends, possibly marketing and outreach data	AI / predictive analytics (unspecified ML/time-series)	Forecasting festival attendance and resource planning	Non-academic source; lack of details on methodology, evaluation metrics or reproducibility

 

3. Conceptual Framework

3.1. Key predictive variables (attendance, demand, logistics, budgets)

The predictive model of art festival planning consists of the establishment and measurement of the main variables directly affecting the performance and sustainability of the festivals. Among them, attendance is the key dependent variable, which summarizes visitor attendance, level of engagement and participation of the audience in general. It is subject to various influences like the price of tickets, marketing intensity, diversity of programs, accessibility of the venue and weather. The variables of demand encompass the velocity of ticket sales, the mentioning of the event in social media, the online search trends, and demographic indication of interest (all are real-time changes in the intent and participation in culture) Frank and Frank (2022). The logistics are the operational aspect of the predictive model and involve the allocation of resources (e.g. staff, facilities, energy, transport) and the spatial organization, which influence the flow of crowds, security and the comfort of visitors. Predictive optimization of logistics will provide balance between the infrastructure readiness and the anticipated demand.

 

3.2. Hypothesized Relationships among Variables

The conceptual model assumes the dynamic interdependence of the variables of attendance, demand, logistics, and budget. Attendance is both a measure and a response measure to future planners of events. The first hypothesis is that there is a positive correlation between attendance and early ticket purchase, social media, and positive weather that increase interest among people. The second hypothesis is that higher levels of marketing spending and audience support in the form of sponsorship, promote wider levels of demand and audience diversity, and this will form a positive feedback loop between turnout and investment. Logistics variables mediated the association between the predicted attendance and the satisfaction of visitors in terms of operational aspects: the well-optimized schedule, infrastructure distribution, and the employees placement options lead to an increase in the engagement and the intentions to attend again. Figure 1 demonstrates the hypothetical correlations between the critical predictive variables in art festival planning. There is a hypothesis that budgetary efficiency is both the cause and the effect: on the one hand, efficient budget management increases logistical readiness, whereas on the other hand, revenue derived out of attendance promotes financial sustainability.

 Figure 1

Figure 1 Conceptual Model of Hypothesized Relationships Among Predictive Variables in Art Festival Planning

The network of inter-variables creates a predictive system, which is very much based on feedback, wherein every aspect affects the others both in the past and within the context. Such non-linear and sequential relationships can best be modeled with machine learning and deep learning models and the hidden dependencies identified, such as how changes in demand due to weather or other forms of social sentiment can affect last-minute sales of tickets. The hypothesized network helps in achieving the goal of the study which is to build data driven and understandable art festival forecasting models.

 

3.3. Model Selection Rationale

The nature of variables and data granularity as well as temporal dependencies informs the nature of model selection in this predictive framework. Time-series models such as ARIMA and Prophet, are included to deal with the recurrent seasonal trends so that short-term and mid-term attendance forecasting can be undertaken using the historical data. These are the models that fit best in the periodic cultural events with a cyclic demand behavior. Non-linearities and cross-variables impacts in event data are however managed by implementing machine learning models that include Random Forest and XGBoost to provide feature interactions, missing data and multi-dimensional prediction between attendance, demand and budget measures. Deep learning models like the Long Short-Term Memory (LSTM) and Transformer-based models are suitable in case of sequential data, like the number of tickets sold each day, number of likes on social media, real-time changes in the environment, etc. The fact that they are able to store long-range temporal dependencies and the ability to capture sentiment-driven changes provide a better insight into the shifting patterns of cultural participation. Also, ensemble and hybrid methods, which combine both interpretability and adaptability of ML with the power and strength of DL, are more accurate and robust in prediction. The choice is not merely performance-based, but it is also focused on the explainability and makes the adoption by the event managers practical.

 

4. Data Collection and Preprocessing

4.1. Data sources: historical event data, ticket sales, weather, social media

The forecasting model combines data sources of heterogeneity to guarantee sound predictive validity. The data on historical events gives a background information about the previous attendance, schedules, performer formation, and venue capacity, which is crucial in determining the trends and seasonality of the past. The information on ticket sales, obtained via online sources and booking services, captures the dynamics of real-time demand in terms of the purchase time and price level, as well as the demographics of a customer. External variables particularly weather data of meteorological APIs (temperature, rainfall, humidity) are important because climatic conditions greatly influence the attendance of outdoor festivals. The analytics of social media based on such platforms as Twitter, Instagram, and Facebook provide the sentiment ratings, the frequency of engagement, and the trending topics used to shape the audience behavior. With the help of the integration of these datasets, a multi-perspective approach can be achieved, i.e. the quantitative data about transactions and the qualitative data about the audience sentiment. This holistic data design can enable the predictive models to put the attendance and budgetary changes into perspective, and adapt in real-time to changing cultural and environmental trends on the performance of the festivals.

 

4.2. Data Cleaning, Normalization, and Feature Extraction

Raw data gathered across various sources usually have a lot of inconsistencies, duplicates and noise which need to be eliminated before modeling. Data cleaning involves elimination of duplicates, the filling of gaps in data, and alignment of the incompatible format among different sources, including ticketing systems and social media APIs. Statistical threshold outlier and z-score outlier methods help in removing incorrect entries. Numerical variables like ticket price, weather intensity and counts of engagements are normalized to uniform ranges so that fair feature contribution can be made in the training process. Derived indicators (cumulative ticket growth rate, sentiment momentum, and historical demand deviation) are used to be combined to feature extraction in order to boost the interpretability and predictive capabilities of a model. These designed characteristics balance raw measurements on the actionable measures, thus supporting effective learning in the ensemble and deep neural architecture with little data imbalance and bias.

 

 

 

5. Predictive Modeling Approach

5.1. Time-series forecasting models (ARIMA, Prophet)

Predictive analytics of art festivals are based on time-series models, which model the temporal relationship and attendance cycles. ARIMA model is especially useful in the case of stationary data which has seasonal and trend elements. It is a combination of autoregressive terms, stationarity differencing, and moving average terms to predict the future values using past attendance or ticket sales. The advantage of ARIMA is that it is very interpretable and it is best suited in short- to mid-term predictions when it comes to planning the festival. It can, however, not work well in non-linear data that are complex and depends on external factors like social media feeling or weather conditions.

 Figure 2

Figure 2 Visualization of Time-Series Forecasting Using ARIMA and Prophet Models for Art Festival Attendance Prediction

 

Prophet, which is a model created by Facebook, adds to ARIMA by being more flexible with non-linear trends that contain several seasonality elements, holiday effects, and missing data. Figure 2 illustrates ARIMA-Prophet time-series predictions of attendance patterns of art festivals. The decomposable nature of prophet allows it to be very useful when dealing with cultural events which are periodic in nature but are also irregularly shaken.

 

5.2. Machine Learning Models (Random Forest, XGBoost)

Machine learning models go well beyond linear trends and non-linear interactions between various input features including the rate at which tickets are sold, changes in temperature, amount spent on marketing and metrics of online engagement. Random Forest (RF) algorithm is an ensemble of decision trees, which is a good performer in structured data, with minimum preprocessing conditions. It also uses multiple trees to reduce overfitting and improve stability, which is why it is highly suitable when the data is heterogeneous where there are both numerical and categorical properties. RF is highly interpretable with the feature importance ranking features, which enables an organizer to discover which features prevail in determining attendance or demand changes. XGBoost (Extreme Gradient Boosting) is a higher-order ensemble methodology, which is an extension of gradient-boosted decision trees. It reduces errors sequentially, which enhances the accuracy of predictions by adaptive rates of learning and regularization. XGBoost is highly effective in dealing with missing values and multi-collinearity, which is why it is most appropriate when dealing with real-life events that are usually irregularly updated. It is faster to compute and more precise, as well as it generalizes better when it deals with unseen data sets in comparison to Random Forest. When incorporated in the planning model of the art festival, these models produce very precise forecasts of attendance, budget expenditure, and resource requirements.

 

 

6. Results and Interpretation

6.1. Attendance prediction outcomes

The prediction model was significantly accurate in predicting the attendance in various editions of the festival. ARIMA, Prophet, and LSTM model developed with a consistent forecasting accuracy, and the Mean Absolute Error (MAE) values were down to 12 percent lower than the baseline approaches. Time-series models were more efficient at capturing seasonal trends, such as weekends and pub holidays, whereas Transformer based predictors were more efficient at identifying social sentiment bursts associated with viral promotions. The hybrid model is proven to be responsive to unforeseen demand changes due to changes in weather or announcing the artist, as demonstrated by high correlation coefficients between the predicted and actual attendance ( r = 0.91).

Table 2

Table 2 Quantitative Results of Attendance Forecasting Models
Model Type	MAE ↓	RMSE ↓	R² Score ↑	Correlation (r) ↑
ARIMA	142.3	188.7	0.82	0.87
Prophet	128.5	171.4	0.85	0.89
Random Forest	112.8	152.3	0.88	0.91
XGBoost	105.6	144.8	0.89	0.92
LSTM	97.2	132.1	0.91	0.93

 

The quantitative analysis of various attendance prediction models implemented in the art festival planning is given in Table 2. ARIMA and Prophet type of traditional time-series models proved to be of moderate accuracy scale since they were effective in predicting seasonal and trend-related changes but were limited in terms of non-linear and multi-factorial effects. Machine learning models such as the Random Forest and the XGBoost greatly enhanced predictive performance due to the ability to model intricate relationship among factors such as the ticket sales, weather and the social media activity. XGBoost showed a significant decrease in the values of MAE and RMSE, which indicates its strength and suitability to different data. Figure 3 displays relative contributions of time-series models that are judged based on R 2 performance. The LSTM (Long Short-Term Memory) model produced the highest level of predictive accuracy with R 2 of 0.91 and correlation of 0.93, exceeding the classical models by a factor of more than 10.

 Figure 3

Figure 3 Proportional Contribution of Time-Series Models Based on R² Performance

 

Its ability to capture time-dependency among sequential data made it exceptionally useful in forecasting short term changes in attendance. These findings substantiate the fact that hybrid and deep learning models have better reliability in the dynamic and real-time forecasting of art festivals.

 

6.2. Resource Allocation and Budget Prediction Insights

The resource optimization and budget forecasting were valuable insights in the machine learning and deep learning models. XGBoost and random forest detected key expenditure drivers such as stage setups, security logistics and marketing expenses which have a high implication on general use of budget. The existence of predictive correlations showed that by aligning staffing and infrastructure to forecasted attendance minimized wastage in operations to the tune of 15%. The models of predicting budgets had a mean R 2 of 0.87 which implies a good ability to explain the dimensions of costs.

Table 3

Table 3 Quantitative Results of Resource and Budget Forecasting
Evaluation Metric	Random Forest	XGBoost	LSTM
MAE (Budget Utilization, ₹’000) ↓	84.2	78.6	72.9
RMSE (Resource Deviation %) ↓	10.8	9.6	8.9
R² Score ↑	0.86	0.88	0.9
Forecast Stability Index ↑	0.82	0.85	0.88

 

Table 3 is a summary of quantitative findings of resource allocation and budget forecasting based on three advanced predictive models: Random Forest, XGBoost, and LSTM. The findings are a clear illustration of progressive increase in forecasting accuracy and stability with increase in model complexity. Figure 4 demonstrates comparative analysis of RF, XGBoost and LSTM forecasting measures.

 Figure 4

Figure 4 Comparative Evaluation of Budget and Resource Forecasting Metrics (RF Vs. Xgboost Vs. LSTM)

 

Random Forest model had a credible base line performance with an R 2 of 0.86 that effectively represented linear and categorical relationships among operational and financial variables. Nonetheless, it had moderate percentages of deviation as there was low temporal adaptability. XGBoost was even more effective, consuming less MAE, smaller RMSE and offering greater generalization when using different scale of events and different budget types.

 

6.3. Demand–Supply Alignment Findings

Multi-source predictive models integration highly helped in synchronizing demand and supply in the operation of the festivals. Predictions by LSTM and Transformer networks have been effective in predicting short-term ticket demand spikes due to social media influencing and the popularity of the artist. The logistic and supply-side predictions were used to match operational responsiveness to material and human resource deployment with these demand spikes. The hybrid system reduced the over-supply in low attendance periods and the shortages in high-demand times, which led to a 17 percent improvement in allocation efficiency. The cross-model analysis showed that the concerted forecasting of the attendance and resource variable facilitated the coordination of these marketing, catering, and transport services. Additionally, the evidence-based information about the timing and level of the audience inflow allowed to adjust to the schedule and save time on queues and enhance visitor satisfaction. The results show that predictive analytics can turn the event planning approach to reactive into the proactive one, ensuring the establishment of the maximum demand-supply ratio throughout the festival lifecycle.

Table 4

Table 4 Quantitative Evaluation of Demand–Supply Synchronization
Parameter	Before Predictive Framework	After Predictive Framework
Resource Allocation Accuracy (%)	78.3	91.6
Demand Surge Response Time (mins) ↓	46.2	29.5
Inventory Utilization Efficiency (%)	74.8	88.2
Service Delay Incidents (per event) ↓	11.4	6.2

 

Table 4 shows the numerical enhancement of demand/supply balance after the suggested predictive model was put into practice. The findings reveal that there are considerable improvements in operations on various performance parameters. Figure 5 indicates that resource and service metrics are improved in operation following the adoption of the predictive framework. Accuracy of resource allocation rose to 91.6% compared to 78.3% indicating that the system can match the staffing, logistical and material allocation with the demand easily predicted.

 Figure 5

Figure 5 Operational Impact of Predictive Framework on Resource and Service Metrics

 

Similarly, the efficiency of inventory utilization increased by 74.8 percent to 88.2 percent, which is evidence of efficient exploitation of the assets on the site like stage equipment, food, and merchandise supplies. There was a significant decrease in demand surge response time, which fell down to 29.5 minutes, indicating how the predictive model was able to foresee the peaks in the crowds and devote resources in advance.

 

7. Conclusion

The analysis of Predictive Models of Art Festival Planning shows that the combination of statistical, machine learning, and deep learning approaches can provide the event management with a significant range of accurate and prompt decisions. Using non-homogenous data sets, including past attendance and ticket sales, weather and social media mood, the framework proposed will give a multivaried view of forecasting, which can be used to develop data-driven planning and dynamical resource distribution. The hybrid model, that is a blend of time-series forecasting (ARIMA, Prophet) and ensemble machine learning (Random Forest, XGBoost) and deep sequential (LSTM, Transformer) models, demonstrated better predictive performance and interpretability. The findings highlight the fact that proper attendance and budget projection is not only essential in enhancing the efficiency of operations, but also enhancing the financial sustainability and satisfaction of the audience. Predictive insights enabled proactive changes in the marketing strategy, staffing and logistics management by enabling the organizers of the festival to make changes in advance based on changing environmental or behavior factors. Furthermore, explainable ML output can be easily integrated and converted into actionable intelligence to non-technical decision-makers to overcome the barriers between computational complexity and managerial usability. Notably, this study stresses the fact that predictive analytics can form the backbone of intelligent cultural event ecosystems, which allows real-time tracking, demand-supply optimization, and long-term strategic forecasting.

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

None.

 

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