Читать книгу Agricultural Informatics - Группа авторов - Страница 21

Meaningful statistics can be extracted from a series data that can be analyzed on time based parameters. This is commonly termed as Time series Analysis and predicts future values based on previously obtained data. Time series analysis can be an important tool used in forecasting the crop yield. The dependent variable yield is time function that can establish the relation between yield and time. Frequency and time domain, parametric or non-parametric methods, linear or nonlinear approaches, univariate and multivariate models are few variants of time series analysis. Spectral analysis are used in frequency domain and wavelet analysis, time domain includes auto-correlation and cross-correlation, parametric approaches use autoregressive or moving average model, non-parametric [30] approaches have covariance or spectrum of the process in the core. A new concept of crop yield under average climate conditions was used in Ref. [31]. The time series techniques was used on the past yield data to set up a forecasting model. The moving average method was used first then regression equation was applied thereafter and finally the difference of the yield and impact of climate on yield was found. Moving average model was concluded as better model for yield forecasting. The model used a small dataset and useful results were obtained.