Usually time series are controlled by generative processes which display changes over time. On many occasions, two or more ge-nerative processes may switch forcing the abrupt replacement of a fitted time series model by another one. We claim that the incor-poration of past data can be useful in the presence of concept shift. We believe that history tends to repeat itself and from time to time, it is desirable to discard recent data reusing old past data to per-form model fitting and forecasting. We address this challenge by introducing an ensemble method that deals with long-memory time series. Our method starts by segmenting historical time series data to identify data segments which present model consistency. Then, we project the time series by using data segments which are close to current data. By using a dynamic time warping alignment func-tion, we try to anticipate concept shifts, looking for similarities be-tween current data and the prequel of a past shift. We evaluate our proposal on non-stationary and non-linear time series. To achieve this we perform forecasting accuracy testing against well known state-of-the-art methods such as neural networks and threshold auto regressive models. Our results show that the proposed method an-ticipates many concept shifts.