Application of Time Series
Article identification
Over time, time-series has continued to evolve, and different scholars have continued to utilize its presence in providing possible solutions and immediate response to various issues. However, to understand further the application part of the time series, there is a need to explore also on separate identifiable articles and research-based articles that have continued to improve the meaning and application of time series. However, a report that presents a research-based approach of time series involves the incorporation of an autoregressive integrated moving average (ARIMA) model.
A model is the right approach that gives a better view of time series data and helps identify the non-zero autocorrelation between successive values of time series. The research aims at getting a specific solution and utilizes different aspects of getting to gain the most desirable outcome. The use of time series is continually evident through the researcher’s ideas continuously throughout the research. Its purpose is the most intriguing aspect that led to the article selection as an accurate representation of the primary application of time series and incorporation of supporting models in analyzing the data available from the time series. The research outcome similarly provides the most efficient use of time series through different environments and approaches.
Summary
The study based on the application of times series in research and prediction of different factor outcomes in various fields. However, the research is based on the agriculture sector and aims at predicting the continued values of sugarcane production in India. Over the past years, sugarcane production has had different patterns, and the department of agriculture aims at predicting the production line of sugarcane for the next five years within the country (Kumar & Anand, 2014 ). However, it utilizes the profound knowledge of time series with available data from the past years, the department retrieves information based on the data and incorporates the use of ARIMA within the study as it continually accounts for the non-zero autocorrelation between values of time series.
Moreover, the research identifies the best possible ARIMA model of (2,1,0) and a prediction followed accurately to ensure the outcomes are well represented. The ARIMA model (2,1,0) fitted the research well as it gave a predictive value for the sugarcane production for the five successive years accurately. The outcomes of the study indicate that output will grow in the following year, then decrease in the next year all through to the fifth year. However, the expected shift will then continue to grow with an average growth rate of three percent in the following years.
Evaluation and role of time series
Time series takes up different parts in research and commonly used as a point of reference as to the possible outcomes of a study. However, within the study, time series took up the role of data analysis and prediction of a possible pattern. It’s through its consistency that the likely outcome of the research arrived. By providing a reasonable structure of the issue and highlighting the underlying function efficiency in predicting the outcomes was made possible in the study. Additionally, time series ensured accuracy and error-free data and which in turn led to a better observation and utilization of a model that gave a correct prediction of the outcome.
Leaving no single gap between the data availed, time series gave a possible outcome. They helped in creating a prior picture of the likely result of the analysis within the research. Moreover, within the issues of the research time-series ensured a thorough understanding of its mechanism, better control would become established. A model that best fits the research was identified and utilized to give the possible outcomes of the study. Continuously, throughout the entire data analysis, time-series impact was practical, and the possible solutions earlier sought arrived at better.