Hideyuki Doi

Using up to 30 years of monthly monitoring records from two ecologically contrasting Japanese lakes, the deep-stratified Lake Biwa and the shallow nutrient-rich Lake Kasumigaura, this study benchmarked two time-series foundation models, the Transformer-based Chronos-T5 and the probabilistic Lag-Llama, against 15 statistical (AR, ARIMA, SARIMA, Prophet), machine learning (Random Forest, XGBoost, KNN, SVR), and deep learning (LSTM, CNN, TCN, and SSA-hybrid) approaches, finding that fine-tuned Chronos-T5 ranked first across all six monitoring sites with R2 > 0.80-1.00 for dissolved oxygen, water temperature, pH, and nutrients, while chlorophyll-a remained below R2 0.

Using up to 30 years of monthly monitoring records from two ecologically contrasting Japanese lakes, the deep-stratified Lake Biwa and the shallow nutrient-rich Lake Kasumigaura, this study benchmarked two time-series foundation models, the Transformer-based Chronos-T5 and the probabilistic Lag-Llama, against 15 statistical (AR, ARIMA, SARIMA, Prophet), machine learning (Random Forest, XGBoost, KNN, SVR), and deep learning (LSTM, CNN, TCN, and SSA-hybrid) approaches, finding that fine-tuned Chronos-T5 ranked first across all six monitoring sites with R2 > 0.80-1.00 for dissolved oxygen, water temperature, pH, and nutrients, while chlorophyll-a remained below R2 0.