Univariate Time Series Model for Iron Production in Australia 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 Iron Production in Australia dataset is a univariate time series situation where we attempt to forecast future outcomes based on past data points.

INTRODUCTION: The problem is forecasting the monthly iron production in Australia. The dataset describes a time-series of weight (in thousand tons) over 40 years (1956-1995), and there are 476 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 56.494. The MLP model processed the same test data with an RMSE of 41.415, 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 (1, 0, 1, 12) processed the validation data with an RMSE of 34.639.

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

Dataset Used: Monthly basic iron production in Australia January 1956 through August 1995

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.