Time series analysis, forecasting and control in SearchWorks catalogScientific Research An Academic Publisher. Box, G. Holden-Day, San Francisco. ABSTRACT: Data Mining has become an important technique for the exploration and extraction of data in numerous and various research projects in different fields technology, information technology, business, the environment, economics, etc. In the context of the analysis and visualisation of large amounts of data extracted using Data Mining on a temporary basis time-series , free software such as R has appeared in the international context as a perfect inexpensive and efficient tool of exploitation and visualisation of time series. This has allowed the development of models, which help to extract the most relevant information from large volumes of data. In this regard, a script has been developed with the goal of implementing ARIMA models, showing these as useful and quick mechanisms for the extraction, analysis and visualisation of large data volumes, in addition to presenting the great advantage of being applied in multiple branches of knowledge from economy, demography, physics, mathematics and fisheries among others.
Time Series Analysis: Forecasting and Control
Pacific Grove, Gregory C. JenkinsWadsworth. ARIMA models describe discrete-time stochastic processes-time series. Therefore, ARIMA models appear as a Data Mining techn.
By reading and understanding the book one should, J, in the end. Transfer functions. Wiley series in probability and statistics. Cabrara.
This notebook introduces a package of Mathematica functions that manipulate autoregressive, integrated moving average ARIMA models. ARIMA models describe discrete-time stochastic processes—time series. The models are most adept at modeling stationary processes. Through differencing, however, these models accommodate certain forms of nonstationary processes as well. Unable to display preview. Download preview PDF.