1. Elfeky, M.G., Aref, W.G., Elmagarmid, A.K.: Periodicity detection in time series databases. IEEE Transactions on Knowledge and Data Engineering17(7), 875–887 (2005)

   Article  Google Scholar 

2. Enders, W.: Applied econometric time series (1995)

   Google Scholar 

3. Gammerman, A., Vovk, V.: Hedging predictions in machine learning. The Computer Journal50(2), 151–163 (2007)

   Article  Google Scholar 

4. Laxhammar, R., Falkman, G.: Sequential conformal anomaly detection in trajectories based on hausdorff distance. In: 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2011)

   Google Scholar 

5. Lei, J., Rinaldo, A., Wasserman, L.: A conformal prediction approach to explore functional data. Annals of Mathematics and Artificial Intelligence, pp. 1–15 (2013)

   Google Scholar 

6. Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research9(2579–2605), 85 (2008)

   Google Scholar 

7. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval, vol. 1. Cambridge University Press, Cambridge (2008)

   Book MATH  Google Scholar 

8. Smith, J., Nouretdinov, I., Craddock, R., Offer, C., Gammerman, A.: Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Sioutas, S., Makris, C. (eds.) Artificial Intelligence Applications and Innovations. IFIP AICT, vol. 437, pp. 271–280. Springer, Heidelberg (2014)

   Chapter  Google Scholar 

9. Tegeler, F., Fu, X., Vigna, G., Kruegel, C.: Botfinder: Finding bots in network traffic without deep packet inspection. In: Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies, pp. 349–360. ACM (2012)

   Google Scholar 

10. Vovk, V., Gammerman, A., Shafer, G.: Algorithmic learning in a random world. Springer (2005)

    Google Scholar 

Download references