Short-term time series wind power predictions are extremely essential for accurate and efficient offshore wind energy evaluation and, in turn, benefit large wind farm operation and maintenance (O&M). However, it is still a challenging task due to the intermittent nature of offshore wind, which significantly increases difficulties in wind power forecasting. In this paper, a novel hybrid model, using unique strengths of Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Deep-learning-based Long Short-Term Memory (LSTM), was proposed to handle different components in the power time series of an offshore wind turbine in Scotland, where neither the approximation nor the detail was considered as purely nonlinear or linear. Besides, an integrated pre-processing method, incorporating Isolation Forest (IF), resampling, and interpolation was applied for the raw Supervisory Control and Data Acquisition (SCADA) datasets. The proposed DWT-SARIMA-LSTM model provided the highest accuracy among all the observed tests, indicating it could efficiently capture complex times series patterns from offshore wind power.