###### Abstract  
In a globalized world, factors such as increasing population, rising production rates, changing consumption habits, and continuous economic growth contribute significantly to climate change. Therefore, successfully forecasting the Ecological Footprint (EF) effectively indicates global sustainable development. Despite the significant role of the EF as one of the indicators of sustainable development, there is a gap in the literature regarding time series methods and forward-looking predictions. To address this gap, Ecological Footprint (EF) forecasting was performed using deep learning methods such as LSTMs, classical time series methods like ARIMA and Holt-Winters, and the developed hybrid ARIMA-SVR model. In the scope of the study, first, a spreadsheet was created using the total Ecological Footprint (EF) worldwide between 1961 and 2022, obtained from the Global Footprint Network database. Second, the forecasting performances of the ARIMA, Holt-Winters, LSTM, and the hybrid ARIMA-SVR models were compared using MAPE and RMSE metrics. Finally, the forecasting performances of the time series models were statistically validated through Wilcoxon Signed-Rank and Friedman tests. The study findings indicate that the proposed ARIMA (1,1,0) model demonstrated better performance with an average MAPE of 2.12%, compared to Holt-Winters (MAPE of 2.27%), LSTM (MAPE of 3.19%), and ARIMA-SVR (MAPE of 2.68%) methods in the test dataset. Additionally, it was observed that the ARIMA model forecasted the EF, which experienced a sudden decrease due to the COVID-19 lockdown, with a lower error compared to other models. These findings highlight the adaptability of the ARIMA model to variable and uncertain conditions. E 2024 14 1479 10.3390/09/10401479 10.  
significant increase in greenhouse gas emissions, are considered incapable of managing their resources sustainably [4]. This situation poses a serious threat to environmental sustainability. Table 1 includes the global Ecological Footprint and biocapacity data for the year 2022 [5]. These data are presented on a per capita basis in global hectares (gha/cap) and encompass components of carbon, crop land, grazing land, forest products, fishing grounds, and built-up land. When Table 1 is examined, the average Ecological Footprint per capita was measured as 2.582 gha/cap in the year 2022. This value indicates that the per capita consumption globally surpasses the capacity allocated for the sustainability of ecosystems, exceeding ecological balance. This situation poses a significant concern for the long-term health of ecosystems and conservation efforts to maintain the balance of nature, as evidenced by instances such as the rapid decline of the Amazon rainforest and the excessive use of water resources. Biocapacity was recorded as 1.510 gha/cap in the year 2022, representing the capacity of nature to withstand human consumption. Ecological Overshoot, on the other hand, indicates the situation where total consumption exceeds biocapacity, and was measured as \(-\)1.072 gha/cap in 2022, meaning that global consumption is below biocapacity. This negative value signifies that the sustainability of the utilized resources falls short, indicating that ecosystems cannot meet the demands of consumption. According to the presented data in Table 1, a balance is observed in crop land and built-up areas, while it is observed that the biocapacity is exceeded in forest grounds and grazing lands. This situation indicates a tendency in sectors such as forestry and livestock to exceed natural resources, often driven by the need to meet increasing demands. The data in Table 1 emphasizes the importance of sustainability efforts and the effective management of natural resources. Increasing measures for ecosystem conservation and considering these data in policy development processes are crucial for a sustainable future. Time-dependent predictions are widely utilized across various fields, serving as a critical tool for comprehending, assessing, and forecasting future events and trends.