Abstract
Time series data mining (TSDM) techniques explores large amount of time series data in search of interesting relationships among variables. The TSDM methods overcome limitations including stationarity and linearity requirements of traditional time series analysis by adapting data mining concepts for analyzing time series data. The Feed Forward Neural Net is one of the most widely used neural nets. In this paper, the Feed Forward Neural Nets architecture is examined and compared with Statistical Time Series Auto regressive integrated moving average (ARIMA) model for prediction of agricultural production. The performance by ANN model and Time series model for prediction are examined using visualization technique and statistical test and the results are illustrated numerically and graphically
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