Time series forecasting plays a key role in several fields, including energy, transportation, weather, etc. Conventional forecasting techniques, along with deep learning-based series forecasting methods such as RNN, CNN, Transformer, and MLP models, are currently thriving. Nevertheless, current approaches struggle to decrease the computational complexity of the model without sacrificing accuracy. To this end, we propose TDMixer, a hybrid lightweight network for long-term series forecasting using time-continuous embedding and magnitude decomposition. (1)To start, we propose time-continuous embedding, a method that converts past timestamps into continuous temporal relationships. This facilitates the model in understanding historical temporal correlation, contributing to improved predictive performance. (2)Additionally, to decrease computational complexity, we suggest utilizing the magnitude decomposition method and constructing a lightweight temporal patterns learner to comprehend diverse temporal patterns. Our investigation uncovers that the significant temporal patterns are predominantly concentrated in the higher magnitude region in the frequency domain. We evaluate TDMixer on five real-world datasets and the experimental results demonstrate its excellent predictive performance and low computational complexity.