With increased integration of wind energy systems, an accurate wind speed forecasting technique is a must for the reliable and secure operation of the power network. Statistical methods such as Auto-Regressive Integrated Moving Average (ARIMA) and hybrid methods such as Wavelet Transform (WT) based ARIMA (WT-ARIMA) model have been the popular techniques in recent times for short-term and very short-term forecasting of wind speed. However, the contribution of the forecasting error due to different decomposed time series on the resultant wind speed forecasting error has yet not been analyzed. This paper, thus explores this shortcoming of the ARIMA and WT-ARIMA models in forecasting of wind speed and proposes a new Repeated WT based ARIMA (RWT-ARIMA) model, which has improved accuracy for very short-term wind speed forecasting. A comparison of the proposed RWT-ARIMA model with the benchmark persistence model for very short-term wind speed forecasting, ARIMA model and WT-ARIMA model has been done for various time-scales of forecasting such as 1min, 3min, 5min, 7min and 10min. This comparison proves the superiority of the proposed RWT-ARIMA model over other models in very short-term wind speed forecasting. (C) 2019 Elsevier Ltd. All rights reserved.