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 American River Riverflow 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 riverflow for the American River at Fair Oaks, California. The dataset describes a time-series of flow volume (in cms) over 55 years (1906-1960), and there are 660 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 90.012. After performing a grid search for the most optimal ARIMA parameters, the final ARIMA non-seasonal order was (1, 0, 0) with the seasonal order being (1, 0, 1, 12). Furthermore, the chosen model processed the validation data with an RMSE of 78.413, 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 riverflow in cms, American River at Fair Oaks, California, October 1906 through September 1960
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.