Ensembles of Neural Network for Telemetry Multivariate Time Series Forecasting
In this paper, we propose to solve the problem of forecasting multivariate time series of telemetry data using neural network ensembles. Approaches to the forming neural network ensembles are analyzed and prediction accuracy is evaluated. The possibility
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United Institute of Informatics Problems of National Academy of Sciences of Belarus, st. Surganova 6, Minsk, Belarus [email protected], [email protected]
Abstract. In this paper, we propose to solve the problem of forecasting multi‐ variate time series of telemetry data using neural network ensembles. Approaches to the forming neural network ensembles are analyzed and prediction accuracy is evaluated. The possibility of training the neural network ensembles is studied for reducing errors of multivariate time series forecasting. Keywords: Forecasting · Artificial neural network · Ensemble of neural networks · Telemetry · Multivariate time series
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Introduction
Space telemetry is a set of technologies that allows remote collection of information about on-board spacecraft subsystems. The subsystems are controlled by analysis of sensor readings that are distributed across submodules. A subsystem state at a particular point in time is described by a vector of sensor values. The time sequence of states is a sequence of vectors of sensor values. Hence, space telemetry data are multivariate time series. One of analysis tasks is the forecasting of such time series. The task of forecasting multivariate time series is generally formulated as follows [1, 2]: from the known current value of the sequence y(k) and the prehistory y(k−1), y(k−2), … , y(k−m) we should evaluate the next value ŷ(k + 1). Each element of the sequence y(k) represents a vector of values at time k. The length sequence m is called as time window. A variety of techniques has been used in short-term forecasting, including regression and time series analysis. Simple regression and multiple linear regressions are frequently used. They have an advantage that they are relatively easy for implementation. However, they are somewhat limited in their ability to forecast in certain situations, especially in the presence of nonlinear relationships between high-level noisy data. Most time series models also belong to the class of linear time series forecasting, because they postulate a linear dependency between the value and its past value. The autoregressive moving average ARMA model and its derivatives are often used for the case of univariate anal‐ ysis However, the artificial neural networks (NNs) often outperform these models in solving complicated tasks [1]. Deep neural networks can also be used [3], but the large training set is needed in this case. © Springer International Publishing AG 2017 V.V. Krasnoproshin and S.V. Ablameyko (Eds.): PRIP 2016, CCIS 673, pp. 53–62, 2017. DOI: 10.1007/978-3-319-54220-1_6
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A. Doudkin and Y. Marushko
The processing and analysis of the telemetry data is accompanied by non-determin‐ istic noises. In this case it is preferred to use NNs technology. The effectiveness of this technology depends on NN architectures and learning methods [1, 4], which requires multiple experiments. There are examples of using NNs in on-board intelligent decision support systems for managing complex dynamic objects and diagnosis of its
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