Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: The purpose of this project is 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 time series situation where we are trying to forecast future outcomes based on past data points.
INTRODUCTION: The problem is to forecast the monthly basic 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 prediction (or persistence) for the dataset resulted in an RMSE of 41.096. After performing a grid search for the most optimal ARIMA parameters, the final ARIMA non-seasonal order was (1, 1, 1) with the seasonal order being (1, 0, 1, 12). Furthermore, the chosen model processed the validation data with an RMSE of 34.639, which was better than the baseline model as expected.
CONCLUSION: For this dataset, the chosen ARIMA model achieved a satisfactory result and should be considered for further modeling.
Dataset Used: Monthly basic iron production in Australia January 1956 through August 1995
Dataset ML Model: Time series forecast with numerical attributes
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.