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Predicting the Direction of Changes in the Values of Time Series for Relatively Small Training Samples

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13301)


Among the tasks of prediction, the task of predicting the signs of increments (direction of change) of the time series process is singled out separately. The essential difference of this problem from the prediction of values (ordinates) of time series implementations is the weak correlation of increments, which, from the point of view of the classical theory of time series forecasting, leads to certain difficulties. First of all, this refers to a non-stationary process, the prediction of which requires constant retraining of the parameters of the predictors used (for example, the weight coefficients of the neural network) over relatively short time intervals, which leads to time costs. This may be unacceptable, for example, when using the prediction of traffic characteristics in telecom networks, when predicting the direction of the gradient in optimization problems, etc.

The paper proposes the use of some results of the theory of random processes for the estimated fast prediction of increments with acceptable accuracy. The proposed procedure of the fast prediction is a simple heuristic rule for predicting the sign of the increment of two neighboring values of a random sequence.


  • Prediction
  • Time series
  • Networks traffic

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Correspondence to Sergey Frenkel .

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Frenkel, S. (2022). Predicting the Direction of Changes in the Values of Time Series for Relatively Small Training Samples. In: Dolev, S., Katz, J., Meisels, A. (eds) Cyber Security, Cryptology, and Machine Learning. CSCML 2022. Lecture Notes in Computer Science, vol 13301. Springer, Cham.

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