Univariate Time Series Model for Cow Milk Production Using TensorFlow

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a time series prediction model and document the end-to-end steps using a template. The Ozone Concentration at Arosa dataset is a univariate time series situation where we attempt to forecast future outcomes based on past data points.

INTRODUCTION: The problem is to forecast the monthly milk production per cow in an agriculture environment. The dataset describes a time-series of milk production (in pounds per cow) over 13 years (1962-1974), and there are 156 observations. We used the first 80% of the observations for training and testing various models while holding back the remaining observations for validating the final model.

ANALYSIS: The baseline persistence model yielded an RMSE of 31.967. The ConvLSTM model processed the same test data with an RMSE of 29.877, which was better than the baseline model as expected. In an earlier ARIMA modeling experiment, the best ARIMA model with non-seasonal order of (1, 1, 1) and seasonal order of (0, 1, 1, 12) processed the validation data with an RMSE of 5.21.

CONCLUSION: For this dataset, the TensorFlow ConvLSTM model achieved an acceptable result, and we should consider using TensorFlow for further modeling.

Dataset Used: Monthly Cow Milk Production January 1962 through December 1975.

Dataset ML Model: Time series forecast with numerical attribute.

Dataset Reference: Rob Hyndman and Yangzhuoran Yang (2018). tsdl: Time Series Data Library. v0.1.0. https://pkg.yangzhuoranyang./tsdl/.

The HTML formatted report can be found here on GitHub.