Use of a new online calibration platform with applications to inertial sensors

In many fields, going from economics to physics, it is common to deal with measurements that are taken in time. These measurements are often explained by known external factors that describe a large part of their behavior. For example, the evolution of the unemployment rate in time can be explained by the behavior of the gross domestic product (the external factor in this case). However, in many cases the external factors are not enough to explain the entire behavior of the measurements, and it is necessary to use so-called stochastic models (or probabilistic models) that describe how the measurements are dependent on each other through time (i.e., the measurements are explained by the behavior of the previous measurements themselves). The treatment and analysis of the latter kind of behavior is known by various names, such as timeseries analysis or signal processing. In the majority of cases, the goal of this analysis is to estimate the parameters of the underlying models which, in some sense, explain how and to what extent the observations depend on each other through time.