@article{48570,
      recid = {48570},
      author = {David, Dimitri Percia, and Moreno, Santiago Anton, and  Maréchal, Loïc, and Maillart, Thomas, and Mermoud, Alain,},
      title = {Forecasting labor needs for digitalization : A bi-partite  graph machine learning approach.},
      pages = {1 volume.},
      abstract = {We use a unique database of digital, and cybersecurity  hires from Swiss organizations and develop a method based  on a temporal bi-partite network, which combines local and  global indices through a Support Vector Machine. We predict  the appearance and disappearance of job openings from one  to six months horizons. We show that global indices yield  the highest predictive power, although the local network  does contribute to long-term forecasts. At the one-month  horizon, the “area under the curve” and the “average  precision” are 0.984 and 0.905, respectively. At the  six-month horizon, they reach 0.864 and 0.543,  respectively. Our study highlights the link between the  skilled workforce and the digital revolution and the policy  implications regarding intellectual property and technology  forecasting.},
      url = {http://tind.wipo.int/record/48570},
      doi = {https://doi.org/10.1016/j.wpi.2023.102193},
}