Time series has been a popular tool for the analysis and forecasting of a large number of data. Very often, the applied approaches forecasts had limited success and the main reason was the lack of statistically significant historical information. We focus our attention on three common series, which are formed from the averaging of data collected over a shorter time interval. These include weekly and biweekly foreign exchange rates, mean hourly wind speed and electric load data. The proposed scheme, which takes advantage of the dominant characteristics of the shorter interval data, produced superior forecasts to those based on conventional approaches based only on historical observations of the target data. In the first two series, the proposed approach generated forecasts that significantly lower to those of the trivial random walk, a benchmark in series dominated by short-term correlation. On the load series, this approach made possible that a simple auto-regressive model returned lower forecasting error compared to a neural network that included special indicators to account for the periodic nature of the data.