  Because of the considerable heterogeneity and complexity of the technological
landscape, building accurate models to forecast is a challenging endeavor. Due
to their high prevalence in many complex systems, S-curves are a popular
forecasting approach in previous work. However, their forecasting performance
has not been directly compared to other technology forecasting approaches.
Additionally, recent developments in time series forecasting that claim to
improve forecasting accuracy are yet to be applied to technological development
data. This work addresses both research gaps by comparing the forecasting
performance of S-curves to a baseline and by developing an autencoder approach
that employs recent advances in machine learning and time series forecasting.
S-curves forecasts largely exhibit a mean average percentage error (MAPE)
comparable to a simple ARIMA baseline. However, for a minority of emerging
technologies, the MAPE increases by two magnitudes. Our autoencoder approach
improves the MAPE by 13.5% on average over the second-best result. It forecasts
established technologies with the same accuracy as the other approaches.
However, it is especially strong at forecasting emerging technologies with a
mean MAPE 18% lower than the next best result. Our results imply that a simple
ARIMA model is preferable over the S-curve for technology forecasting.
Practitioners looking for more accurate forecasts should opt for the presented
autoencoder approach.
