Accurate tourism demand forecasting is hindered by limited historical data and complex spatiotemporal dependencies among tourist origins. A novel forecasting framework integrating virtual sample generation and a novel Transformer predictor addresses constraints arising from restricted data availability. A spatiotemporal GAN produces realistic virtual samples by dynamically modeling spatial correlations through a graph convolutional network, and an enhanced Transformer captures local patterns with causal convolutions and long-term dependencies with self-attention,eliminating autoregressive decoding. A joint training strategy refines virtual sample generation based on predictor feedback to maintain robust performance under data-scarce conditions. Experimental evaluations on real-world daily and monthly tourism demand datasets indicate a reduction in average MASE by 18.37% compared to conventional Transformer-based models, demonstrating improved forecasting accuracy. The integration of adaptive spatiotemporal sample augmentation with a specialized Transformer can effectively address limited-data forecasting scenarios in tourism management.

Genotype-to-Phenotype (G2P) prediction plays a pivotal role in crop breeding, enabling the identification of superior genotypes based on genomic data. Rice (Oryza sativa), one of the most important staple crops, faces challenges in improving yield and resilience due to the complex genetic architecture of agronomic traits and the limited sample size in breeding datasets. Current G2P prediction methods, such as GWAS and linear models, often fail to capture complex non-linear relationships between genotypes and phenotypes, leading to suboptimal prediction accuracy. Additionally, population stratification and overfitting are significant obstacles when models are applied to small datasets with diverse genetic backgrounds. This study introduces the Learnable Group Transform (LGT) method, which aims to overcome these challenges by combining the advantages of traditional linear models with advanced machine learning techniques. LGT utilizes a group-based transformation of genotype data to capture spatial relationships and genetic structures across diverse rice populations, offering flexibility to generalize even with limited data. Through extensive experiments on the Rice529 dataset, a panel of 529 rice accessions, LGT demonstrated substantial improvements in prediction accuracy for multiple agronomic traits, including yield and plant height, compared to state-of-the-art baselines such as linear models and recent deep learning approaches. Notably, LGT achieved an R^2 improvement of up to 15\% for yield prediction, significantly reducing error and demonstrating its ability to extract meaningful signals from high-dimensional, noisy genomic data. These results highlight the potential of LGT as a powerful tool for genomic prediction in rice breeding, offering a promising solution for accelerating the identification of high-yielding and resilient rice varieties.