In one example, the method may include storing previously recorded temporal patterns of time-series data, determining a set of optimal bin boundaries based on the previously recorded temporal patterns, where the set of optimal bin boundaries divide the observed range of time-series data into a ...
Obtain predictors and targets for the training data using theprocessDatafunction defined in theProcess Datasection of the example. The function processes the data such that each time step is an observation and the predictors for each observation are the historical time series data of ...
This example shows how to perform multivariate time series forecasting of data measured from predator and prey populations in a prey crowding scenario. The predator-prey population-change dynamics are modeled using linear and nonlinear time series models. Forecasting performance of these models is ...
For example, given (1){Xt}={X1,X2,⋯,XN}, we can construct the bivariate time series (2){Zt}={Z1,Z2,⋯,ZN−2}={(X2X3),(X3X4),⋯,(XN−1XN)}, Another option is to consider two physically different univariate variables and combine them. For example, consider wind ...
For example, time series data from an epidemic model may include the number of patients, the number of healthy people, infection rate and the immunization rate, etc. The severity of epidemic cannot be judged by partial characteristics. Therefore, a more reasonable method is to comprehensively ...
For example, in the field of intelligent medical care, the patients’ status is monitored in real-time through smart devices to detect emergencies in time [1]; in the field of intelligent transportation, the data of the Internet of Vehicles is used to monitor the vehicle status to ensure ...
In Section 7.1 we introduce two sets of bivariate time series data for which we develop multivariate models later in the chapter. In Section 7.2 we discuss the basic properties of stationary multivariate time series, namely the mean vector μ = E X t and the covariance matrices Γ( h ) =...
Univariate time series:Only the history of one variable is collected as input for the analysis. For example, only the temperature data collected over time from a sensor measuring the temperature of a room every second. Multivariate time series:The history of multiple variables is collected as inpu...
Multivariate time series (MTS) arise when multiple interconnected sensors record data over time. Dealing with this high-dimensional data is challenging for every classifier for at least two aspects: First, an MTS is not only characterized by individual feature values, but also by the interplay of...
Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw ...