However it's worth noting that Fig. \ref{391527} shows some notable differences as well between the technology records extracted from the Derwent Innovation Index and the Questel-Orbit results. There are several reasons for this. Most significantly, these two databases will not consist of exactly the same records, or the same volume of records. This was apparent based on the record counts provided in \cite{Gao_2013} which resulted in several thousand less records for the exact same search query structure and filtration steps. Secondly, several years have passed since the original study, and record counts for later years will have been amended in the passing time as more records have been accounted for. Furthermore, there may also be discrepancies between how the two databases account for patent families within their internal methodologies, and the exact functioning of the search algorithms used to identify records. As such, whilst many of the peaks in the Questel-Orbit data correspond to equivalent peaks in the Derwent Innovation Index data, not all peaks align perfectly. There is also an observable difference in the trend extracted for the number of cited patents per year, which seems considerably higher based on the Questel-Orbit data. To verify this, a separate examination of patent citation counts using Excel pivot tables for several sample batches of patent records was conducted, which found that the expected number of citations extracted using the MATLAB script matched to a good level those found when using the Excel-based procedure. Consequently, the discrepancy here between Questel-Orbit results and Derwent Innovation Index records could be as a result of recording differences when addressing citations in these two databases. However, it is not felt that this discrepancy will significantly impact results as the analysis that follows is based on amplitude normalised trends rather than absolute values, and as such there will be notably less variation in the actual values used (see Fig. \ref{456301} and Fig. \ref{879179} for an illustration of this).
With these discrepancies noted, the training technologies correspond to the timescales shown for Cathode Ray Tubes (CRT), nuclear power, solar photovoltaics, wind electricity, mobile phones, thin film transistor liquid crystal displays (TFT-LCD), and Compact Fluorescent Light bulbs (CFLs) provided in Fig. \ref{176117}. Having specified the training technology profiles to use, the MATLAB script (provided in Appendix XX) then smooths both training and test time series based on a three-year moving average, and normalises the amplitude as per the original study, as shown in Fig. \ref{456301} and Fig. \ref{879179}.