A Synchrosqueezed Wavelet Transform Assisted Machine Learning Framework for Time Series Forecasting

The attention of researchers in the field of signal analysis has recently been captured by several kinds of reassignment techniques. Among the reallocation methods the Synchrosqueezing approach maps a continuous wavelet transform from the time-scale to the time-frequency plane, allowing a much more definite and consistent representation of the frequency content of a signal. Its mathematical foundations prove that the Synchrosqueezing Wavelet Transform is directly related to the Empirical Mode Decomposition EMD, since both methods allow the decomposition of a signal having finite energy in its intrinsic mode functions, each having time-varying frequency and amplitude. We develop a fast C++ implementation of the Synchrosqueezed Wavelet Transform SST suitable for the synchronic extrusion of instantaneous frequency information from univariate time series. Such module, totally configurable and adaptable to input datasets having different statistical properties, can be coupled with a predictor system to the purpose of real value forecasting. We project such a composite application and plan to research the forecasting accuracy achievable, using different types of non parametric statistical estimators or neural regressors.