An investment portfolio consists of a set of financial assets, such as stocks, fixed-income securities, mutual funds, and real estate, held to achieve diversification and to optimize returns. Accurate asset forecasting provides investors with valuable information to support decision-making. Although existing studies have proposed models for forecasting and portfolio optimization, most rely mainly on traditional techniques and metaheuristic approaches. This work introduces ESIPO (Ensemble Strategies for Investment Portfolio Optimization), a methodology that integrates deep learning and metaheuristic algorithms to perform asset forecasting and investment portfolio optimization. The dataset is obtained from the S&P 500 index, one of the main stock markets. To enhance forecasting accuracy, ESIPO combines five methods from the top-performing models of the international M4 competition: (a) ARIMA (AutoRegressive Integrated Moving Average) and ETS (the statistical exponential-smoothing state-space), which represent classical statistical approaches; (b) FFORMA (Feature-based FORecast Model Averaging) and JAGANATHAN, two ensemble-based methods; (c) CNN (Convolutional Neural Network), which is one of the most common deep learning models. ESIPO improves the forecast performance of the portfolio by applying the TAIPO (Threshold Accepting Investment Portfolio Optimization) metaheuristic to select the best assets and optimize portfolio composition. The results obtained 45% of improvement according to the Sharpe Ratio metric.

Polysaccharide-based films represent a sustainable alternative for the development of intelligent packaging systems capable of monitoring food quality through colorimetric changes. In this study, films formulated with chitosan, starch, glycerol, and Hibiscus sabdariffa extract were evaluated as potential freshness indicators for pork meat. The films were prepared using different extract concentrations and stored at 6 and 25 °C before and after contact with pork samples for 36 h. Their colorimetric response was quantified through digital image analysis using the RGB color model combined with the K-means clustering algorithm. The relationships among the experimental variables were investigated using descriptive statistics, Spearman correlation analysis, and feature relevance metrics, including Information Gain, Gain Ratio, and Analysis of Variance (ANOVA). The results demonstrated that polymer blend concentration was the most influential factor in differentiating film performance, whereas storage time and extract concentration significantly affected both the colorimetric response and pH values. Multivariable analysis revealed distinct patterns associated with film stability and the physicochemical changes induced by contact with pork meat during storage. These findings highlight the potential of integrating polysaccharide-based materials with data science approaches to support the design, optimization, and performance evaluation of intelligent packaging systems for real-time freshness monitoring of meat products. Beyond the specific application evaluated, the proposed methodology demonstrates how data science tools can support the objective assessment of intelligent biodegradable films, facilitating their future implementation in real-time food quality monitoring systems.