PREDICTIVE MARKET TRENDS IN PRINTING AND PHOTOGRAPHY

Authors

  • Vibhor Mahajan Centre of Research Impact and Outcome, Chitkara University, Rajpura 140417, Punjab, India
  • Chaitanya Joshi Assistant Professor, Department of Film and Television, Parul Institute of Design, Parul University, Vadodara, Gujarat, India
  • Pooja Goel Associate Professor, School of Business Management, Noida International University, India
  • Karthik K Assistant Professor, Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (Deemed to be University), Tamil Nadu, India
  • Dipali Kapil Mundada Department of Engineering, Science and Humanities, Vishwakarma Institute of Technology, Pune 411037, Maharashtra, India
  • Dr. Dolly Misra Assistant Professor (Grade II), Department of Management, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (Deemed to be University), Tamil Nadu, India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6919

Keywords:

Predictive Analytics, Printing Industry, Photography Market, Time-Series Forecasting, AI-Driven Market Trends

Abstract [English]

The printing and photography sectors are changing very fast due to digitalization, automation and using data to make decisions. The analysis of market trends which can be predicted as a key to the analysis of the demand fluctuations, the technological use, and the changing preferences of consumers in such visual-oriented sectors. This paper will examine the predictive market trends in printing and photography by using a combination of economic indicators, technological drivers and behavioral data into a single analysis tool. To capture both the macroeconomic and the micro-level market dynamics, the study uses heterogeneous sources of data, which are industry reports, sales records, online platforms, and social media trend signals. Premature preprocessing and feature engineering are used to derive temporal, economic and behavioral cues to express pricing frameworks, production expenses, personalization demand, sustainability inclinations, and transformations in visual culture. Several different predictive modeling approaches will be considered, including classical time-series like ARIMA, SARIMA, Prophet, or machine learning models like regression, random forest, and gradient boosting or deep learning models like LSTM, GRU, or transformer-based predictors. Comparative analysis reveals that hybrid, as well as deep learning models, are strong in terms of capturing non-linear trends and long term dependencies of creative markets.

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Published

2025-12-28

How to Cite

Mahajan, V., Joshi, C., Goel, P., Karthik K, Mundada, D. K., & Misra, D. (2025). PREDICTIVE MARKET TRENDS IN PRINTING AND PHOTOGRAPHY. ShodhKosh: Journal of Visual and Performing Arts, 6(5s), 329–339. https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6919