Knowledge about future traffic in backbone optical networks may greatly improve a range of tasks that Communications Service Providers (CSPs) have to face. This work proposes a procedure for long-term traffic forecasting in optical networks. We formulate a long-terT traffic forecasting problem as an ordinal classification task. Due to the optical networks’ (and other network technologies’) characteristics, traffic forecasting has been realized by predicting future traffic levels rather than the exact traffic volume. We examine different machine learning (ML) algorithms and compare them with time series algorithms methods. To evaluate the developed ML models, we use a quality metric, which considers the network resource usage. Datasets used during research are based on real traffic patterns presented by Internet Exchange Point in Seattle. Our study shows that ML algorithms employed for long-term traffic forecasting problem obtain high values of quality metrics. Additionally, the final choice of the ML algorithm for the forecasting task should depend on CSPs expectations.

Considering the low accuracy and low efficiency of the traditional calibration method for base strain sensitivity of accelerometers, a novel base strain sensitivity calibration system with steady harmonic excitation is proposed. The required cantilever beam for calibration is driven by an electromagnetic exciter to generate a base strain varying in a steady harmonic pattern. By applying a Wheatstone bridge circuit, the generated strain with low distortion can be measured. The measurement system with a compensation function can automatically calibrate the base strain sensitivity. The amplitude linearity and frequency response characteristics of the base strain sensitivity in two accelerometers are obtained experimentally, and the uncertainty in the results is 2% ( k = 2).