Abstract: A hyper-parameter configuration method of time-series forecasting model comprises storing N datasets respectively corresponding to N products; determining a forecasting model; and performing a hyper-parameter searching procedure. The hyper-parameter searching procedure comprises generating M sets of hyper-parameters; applying each set of hyper-parameters to the forecasting model; training and validating the forecasting model respectively according to two strategies to generate two error arrays, wherein the two strategies selects the training dataset and the validation dataset from N datasets in different two data dimensions, performing a weighting computation or an ordering operation according to two weights and the two error arrays and searching for a target set of hyper-parameters, wherein two error values corresponding to the target set of hyper-parameters in the two error arrays are two relative minimums.

Abstract: A method of heart failure prediction comprising obtaining a raw electrocardiogram (ECG) signal by a sensor, generating a clean ECG signal according to the raw ECG signal by a pre-processing circuit, performing, by a feature extraction circuit, a principal component decomposition and a heart rate feature analysis according to the clean ECG signal to generate a feature vector with a plurality of features, and generating a prediction to indicate whether the heart failure will happen in a specified period according to the feature vector by a predicting model circuit.

Abstract: A hierarchical time-series prediction method is adapted to a plurality of reconciled predictions of a plurality of nodes of a hierarchical structure. The plurality of nodes have a plurality of time-series respectively, the plurality of reconciled predictions correspond to the plurality of time-series, the plurality of nodes comprises a plurality of bottom nodes, and the hierarchical time-series prediction method comprises: generating a plurality of individual predictions corresponding to the plurality of time-series respectively by a plurality of predictive models; generating a plurality of bottom-level predictions corresponding to the plurality of bottom nodes according to the plurality of individual predictions and an encoder network; and generating the plurality of reconciled predictions according to the plurality of bottom-level predictions and a decoder associated with the hierarchical structure.

Abstract: A signal quality index evaluation circuit, comprises: a surrounding sensor; a zero-phase filter; and an evaluation circuit. The surrounding sensor senses its surrounding to generate a reference correction signal. The zero-phase filter is configured to generate a clean biological signal according to a biological signal and the reference correction signal, wherein the clean biological signal includes a plurality of period signals, and each one of the period signals has a biological value. The evaluation circuit is configured to calculate norm range according to the clean biological signal and one or more of the biological values of the period signals, and determine a difference between each one of the biological values corresponding to each one of the period signals and the norm range, the evaluation circuit is further configured to calculate and output a signal quality index according to the differences.