Tag: time series

# Univariate Time Series Modeling Template Using TensorFlow Version 1

As I work on practicing and solving machine learning (ML) problems, I repeatedly re-use a programming set of steps and activities.

Thanks to Dr. Jason Brownlee’s suggestions on creating a machine learning template, I have pulled together a project template that can be used to support time series analysis using the TensorFlow framework and Python.

Version 1 of the TensorFlow time series template replicates many code segments within Dr. Brownlee’s blog post “Deep Learning Models for Univariate Time Series Forecasting”. The plan is to build a script for modeling future projects by adapting the example workflow presented in the blog.

The TensorFlow time series template is on the Analytics Project Templates page.

# 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.

# 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.

# Univariate Time Series Model for USA Housing Starts 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 Housing Starts 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 monthly housing starts in the US. The dataset describes a time-series of housing sales over 11 years (1965-1975) in the US, and there are 132 monthly observations. We used the first 80% of the observations for training and testing various models, holding back the remaining observations to validate the final model.

ANALYSIS: The baseline persistence model yielded an RMSE of 28,010. The ConvLSTM model processed the same test data with an RMSE of 8,940, which was better than the baseline model as expected. In an earlier ARIMA modeling experiment, the best ARIMA model with non-seasonal order of (4, 0, 4) and seasonal order of (1, 0, 1, 12) processed the validation data with an RMSE of 6,276.

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

Dataset Used: U.S. Housing Starts 1965 – 1975.

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.

# Univariate Time Series Model for USA Housing Sales 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 Housing Sales 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 monthly housing sales in the US. The dataset describes a time-series of housing sales over 11 years (1965-1975) in the US, and there are 132 monthly 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 13.627. The CNN model processed the same test data with an RMSE of 6.959, 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, 0, 0) and seasonal order of (1, 0, 1, 12) processed the validation data with an RMSE of 3.556.

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

Dataset Used: Sales of U.S. Houses 1965 – 1975.

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.