SparseTSF is a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources. The model employs the Cross-Period Sparse Forecasting technique, which simplifies the forecasting task by decoupling periodicity and trend in time series data. This technique involves downsampling the original sequences to focus on cross-period trend prediction, effectively extracting periodic features while minimizing the model's complexity and parameter count. SparseTSF uses fewer than 1k parameters to achieve competitive or superior performance compared to state-of-the-art models. It also demonstrates remarkable generalization capabilities, making it well-suited for scenarios with limited computational resources, small samples, or low-quality data. The model's key contribution is the Cross-Period Sparse Forecasting technique, which downsamples the original sequences to focus on cross-period trend prediction, effectively extracting periodic features while minimizing the model’s complexity and parameter count. Based on this technique, the SparseTSF model requires fewer than 1k parameters, significantly reducing the computational resource demand of forecasting models. The proposed SparseTSF model not only attains competitive or surpasses state-of-the-art predictive accuracy with a remarkably minimal parameter scale but also demonstrates robust generalization capabilities. SparseTSF is designed to handle data with a stable main period, demonstrating enhanced feature extraction capabilities and an extremely lightweight architecture. However, it may not be as effective in scenarios involving ultra-long periods or multiple periods. The model's lightweight nature and ability to extract periodic features make it suitable for deployment in computation resource-constrained environments. SparseTSF also exhibits potent generalization capabilities, opening new possibilities for applications in transferring to small samples and low-quality data scenarios. It stands as another milestone in the journey towards lightweight models in the field of long-term time series forecasting.SparseTSF is a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources. The model employs the Cross-Period Sparse Forecasting technique, which simplifies the forecasting task by decoupling periodicity and trend in time series data. This technique involves downsampling the original sequences to focus on cross-period trend prediction, effectively extracting periodic features while minimizing the model's complexity and parameter count. SparseTSF uses fewer than 1k parameters to achieve competitive or superior performance compared to state-of-the-art models. It also demonstrates remarkable generalization capabilities, making it well-suited for scenarios with limited computational resources, small samples, or low-quality data. The model's key contribution is the Cross-Period Sparse Forecasting technique, which downsamples the original sequences to focus on cross-period trend prediction, effectively extracting periodic features while minimizing the model’s complexity and parameter count. Based on this technique, the SparseTSF model requires fewer than 1k parameters, significantly reducing the computational resource demand of forecasting models. The proposed SparseTSF model not only attains competitive or surpasses state-of-the-art predictive accuracy with a remarkably minimal parameter scale but also demonstrates robust generalization capabilities. SparseTSF is designed to handle data with a stable main period, demonstrating enhanced feature extraction capabilities and an extremely lightweight architecture. However, it may not be as effective in scenarios involving ultra-long periods or multiple periods. The model's lightweight nature and ability to extract periodic features make it suitable for deployment in computation resource-constrained environments. SparseTSF also exhibits potent generalization capabilities, opening new possibilities for applications in transferring to small samples and low-quality data scenarios. It stands as another milestone in the journey towards lightweight models in the field of long-term time series forecasting.