
Package `KernGPLM`
... Helps to define a grid for kernel denity or regression estimates (univariate or multivariate). Usage create.grid(grid.list, sort=TRUE) Arguments grid.list ...
... Helps to define a grid for kernel denity or regression estimates (univariate or multivariate). Usage create.grid(grid.list, sort=TRUE) Arguments grid.list ...
Artificial Intelligence Tool and Electronic Systems Used to
... research areas; optics and photonics are not the exception. For our group, the utilization of computers; and electronic systems and devices has permitted to develop several projects to control processes. Some of them will be described in this chapter. In section 1, the development of irradiance mete ...
... research areas; optics and photonics are not the exception. For our group, the utilization of computers; and electronic systems and devices has permitted to develop several projects to control processes. Some of them will be described in this chapter. In section 1, the development of irradiance mete ...
Knowledge Based System and Database Management System: An
... A major reason for the integration of these two technologies is the realization that these are complementary technologies with the potential for enhancing the capabilities of AI systems with database features, and database management systems (DBMS) with learning and other features found in AI system ...
... A major reason for the integration of these two technologies is the realization that these are complementary technologies with the potential for enhancing the capabilities of AI systems with database features, and database management systems (DBMS) with learning and other features found in AI system ...
Genomic analyses with emphasis on single-step
... Random regression model (RRM) Useful for analyses of traits that are continuous with time (e.g., milk or weight) or other variables (e.g., temperature-humidity index or herd-level). The key part of RRM is the use of several parameters to describe variability of continuous traits. Functions that use ...
... Random regression model (RRM) Useful for analyses of traits that are continuous with time (e.g., milk or weight) or other variables (e.g., temperature-humidity index or herd-level). The key part of RRM is the use of several parameters to describe variability of continuous traits. Functions that use ...
IJAI-13 - aut.upt.ro
... Segmentation problems are based on the division of a given curve in a set of n segments (being each of these segments represented by a linear model, which points to another common naming convention for this process: piecewise linear representation, PLR) minimizing the representation error. This is a ...
... Segmentation problems are based on the division of a given curve in a set of n segments (being each of these segments represented by a linear model, which points to another common naming convention for this process: piecewise linear representation, PLR) minimizing the representation error. This is a ...
Lectures on Artificial Intelligence – CS364 Knowledge Engineering
... • As an example, we consider a problem of predicting the market value of a given house based on the knowledge of the sales prices of similar houses. • In this problem, the inputs (the house location, living area, number of bedrooms, number of bathrooms, land size, type of heating system, etc.) are w ...
... • As an example, we consider a problem of predicting the market value of a given house based on the knowledge of the sales prices of similar houses. • In this problem, the inputs (the house location, living area, number of bedrooms, number of bathrooms, land size, type of heating system, etc.) are w ...
KS3 Maths Learning Objectives
... Use simple formulae from mathematics and other subjects; substitute positive integers into linear expressions and formulae and, in simple cases, derive a formula. Use formulae from mathematics and other subjects; substitute integers into simple formulae, including examples that lead to an equation t ...
... Use simple formulae from mathematics and other subjects; substitute positive integers into linear expressions and formulae and, in simple cases, derive a formula. Use formulae from mathematics and other subjects; substitute integers into simple formulae, including examples that lead to an equation t ...
AI Magazine - Intelligent and Mobile Agents Research Group
... that is coupled with an increased reliance on this data by health practitioners and patients. The main boundaries to fully leveraging the data are its sophisticated management, analysis, and use of results. AI techniques are very well suited to expand these boundaries, thus facilitating advances in ...
... that is coupled with an increased reliance on this data by health practitioners and patients. The main boundaries to fully leveraging the data are its sophisticated management, analysis, and use of results. AI techniques are very well suited to expand these boundaries, thus facilitating advances in ...
Time series

A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called ""time series analysis"", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.).