| 摘要 |
A generalized autoregressive integrated moving average (ARIMA) model for use in predictive analytics of time series is based upon creating all possible ARIMA models (by knowing a priori the largest possible values of the p, d and q parameters forming the model), and utilizing the results of at least two different performance measures to ultimately choose the ARIMA(p,d,q) model that is most appropriate for the time series under study. The method of the present invention allows each parameter to range over all possible values, and then evaluates the complete universe of all possible ARIMA models based on these combinations of p, d and q to find the specific p, d and q parameters that yield the “best” (i.e., lowest value) performance measure results. This generalized ARIMA model is particularly useful in predicting future operating hours of power plants and scheduling maintenance events on the gas turbines at these plants. |
| 主权项 |
1. A method of scheduling events for industrial equipment using an autoregressive integrated moving average (ARIMA) model for predicting future operating hours based on a times series of past operating hours of the industrial equipment, comprising defining a maximum possible value for each parameter p, d, q of an ARIMA(p,d,q) model, p defining a number of autoregressive terms to include in the ARIMA model, d defining a number of differencing operations to perform in the ARIMA model, and q defining a number of moving average terms to include in the ARIMA model, the maximum possible values identified as pr, dr, and qr;
for all possible combinations of p from 0 to pr, d from 0 to dr, and q from 0 to qr, performing the following steps:
determining a set of ARIMA coefficients associated with a training interval of the time series;predicting a set of N future hours based on the determined coefficients;computing at least one performance measure of the predicted set of N future operating hours with respect to actual time series data; andranking all possible combinations of ARIMA(p,d,q) based on the computed performance measures; selecting a preferred set of (p, d, q) parameters based on the ranking; generating a predicted future time series of industrial operating hours using the selected ARIMA(p,d,q) model; and scheduling events based on the predicted future operating hours. |
