Univariate Time Series Model for Annual Immigration into USA 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 Annual Immigration into USA 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 annual number of people immigrating to the United States. The dataset describes a time-series of people (in thousands) over 143 years (1820-1962), and there are 143 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 52,116. The LSTM model processed the same test data with an RMSE of 38,031, which was better than the baseline model as expected. In an earlier ARIMA modeling experiment, the best ARIMA model with non-seasonal order of (0, 1, 2) processed the validation data with an RMSE of 61,789.

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

Dataset Used: Annual immigration into the United States, 1820-1962.

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.