Parametric and Non-Parametric Modelling of Time Series — An Empirical Study

Gender and Food Resourcism in Sahelian Zone of Cameroon

“…It is used to indicate relationships between several variables. The calculation of the Pearson correlation coefficient between two variables is based on the calculation of their covariance (Chen and Popovich 2002). It thus gives an indication of the intensity, form and direction of the relationship.…”

A Multiple Algorithm Approach to the Analysis of GNSS Coordinate Time Series for Detecting Geohazards and Anomalies

“…Since time series have an explicit order dependence between observations, researchers have used this to analyze the temporal dependency between observation values, where the goal is to develop a mapping function that expresses the relation between the input variables (e.g., past values in the time series) into output variables (e.g., future values in a time series). The structure of this function can either be considered known, parametric models (e.g., autoregressive and moving average models) (Cohen, 2014;Connor et al, 1994), or unknown, nonparametric models (Chen et al, 1997).…”

Robust ensemble learning framework for day-ahead forecasting of household based energy consumption

“…Common techniques for energy consumption forecasting include time series models [26], Exponential Smoothing [27], Linear Regression [28], Generalized Additive Models [29,30], and Functional Data Analysis [13]. Such classical methods, also referred to as non-machine learning methods, have been comprehensively studied in the literature, and a useful overview of their common attributes can be found in [31]. The previously mentioned techniques have been demonstrated on aggregated demand studies.…”