Time-Series Forecasting Patents in Mexico Using Machine Learning and Deep Learning Models

Juan-Carlos Gonzalez-Islas, Ernesto Bolaños-Rodriguez, Omar-Arturo Dominguez-Ramirez, Aldo Márquez-Grajales, Víctor-Hugo Guadarrama-Atrizco, Elba-Mariana Pedraza-Amador

Patenting is essential for protecting intellectual property, fostering technological innovation, and maintaining competitive advantages in the global market. In Mexico, strategic planning in science, technology, and innovation requires reliable forecasting tools. This study evaluates computational models for predicting applied and granted patents between 1990 and 2024, including statistical (ARIMA), machine learning (Regression Trees, Random Forests, and Support Vector Machines), and deep learning (Long Short-Term Memory, LSTM) approaches. The workflow involves historical data acquisition, exploratory analysis, decomposition, model selection, forecasting, and evaluation using the Root Mean Square Error (RMSE), the determination coefficient (R2), and the Mean Absolute Percentage Error (MAPE) as performance metrics. To ensure generalization and robustness in the training stage, we use the cross-validation rolling origin. On the test stage, LSTM achieves the highest accuracy (RMSE = 106.91, R2=0.97, and MAPE = 0.63 for applied patents; RMSE = 283.20, R2=0.93, and MAPE = 2.65 for granted patents). However, cross-validation shows that ARIMA provides more stable performance across multiple scenarios, highlighting a trade-off between short-term accuracy and long-term reliability. These results demonstrate the potential of machine learning and deep learning as forecasting tools for industrial property management.