Univariate Time Series Model for USA Air Passenger Miles 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 USA Air Passenger Miles dataset is a univariate time series situation where we attempt to forecast future outcomes based on past data points.

ANALYSIS: The baseline persistence model yielded an RMSE of 1.581. The CNN-LSTM model processed the same test data with an RMSE of 1.109, 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, 2) and seasonal order of (1, 1, 1, 12) processed the validation data with an RMSE of 0.437.

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

Dataset Used: Monthly U.S Air Passenger Miles January 1960 through December 1977.

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.