<p>Accurate forecasting of time series data is essential in many fields. However, real-world time series are often characterized by noise, non-stationarity and multiscale temporal dependencies, which collectively reduce forecasting performance. To address these challenges, MultiScaleWave, a deep learning framework based on time series decomposition, is proposed for univariate forecasting. The MultiScaleWave model first applies multi-level discrete wavelet transforms to decompose the series into multiscale temporal components. Each component is modeled by a granularity-adaptive module, and the outputs are then fused to generate an informative representation for final forecasting. The MultiScaleWave model has been validated on benchmark datasets and achieves superior performance compared to competitive baselines. The results demonstrate the effectiveness and generalizability of the proposed approach.</p>