We study analysis and forecasting strategies for time series based on multiscale analysis. The method is illustrated for a set of data collecting several years of booking information from the air traffic company Lufthansa Systems GmbH, Berlin. In particular, we deal with data where the variability of the forecast units leads to different problems in computing. We consider several years of subsequent data and apply a wavelet decomposition over a certain number of scales. In wavelet domain the data are subdivided in low and high frequency parts. Forecast values on each scale are calculated, the inverse wavelet transform yields a forecast for the whole signal. In the present paper we describe the analysis of several historical booking data sets from Lufthansa Systems GmbH dealing with data over a period of 4 years. Based on the wavelet transform we apply a forecast to the data. The forecast itself depends on the behaviour of the data on each scale. The wavelet decomposition can be used to reveal trends and seasonal influences.