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This repository stores the documentation for new approaches to predicting prediction bands for full paths of tropical cyclones (TCs) using just a few initial points. We leverage extensions on regression models to generate curves, combined with multiple approaches to create prediction bands for the full paths. These approaches utilize kernel density estimation, utilize depth-based relationships between curves, and also leverage geometric properties of the space. This project was specifically designed with the use of the HURDAT 2 data set from the National Oceanic and Atmospheric Administration.

Table of Contents:

  1. Replication of Results

    1.1. makefile usage

    1.2. Special Script Guidelines

  2. TCpredictionbands package


  4. Contributors

1 Replication of Results

As is generally the case in papers today, our analysis is replicable. The files in the main/ folder store the necessary files to run our analysis. The viewer should note that the analysis pipeline takes a long while and should decide if they would like to replicate the full results or just to test out the analysis on a few samples. Additionally, the R package TCpredictionbands (see associated section), which is included in this repo, provides the user with the ability to analyze different generated curves with our prediction band approaches and also TC paths that were not available when we developed the package.

Files in main/ are titled in the following format:


where the __ can be in the following forms:

  • {integers}: main scripts
  • R{integers}: scripts to peform results analysis, and well as scripts to create results and discussion figures and tables
  • F{integers}: files to create figures that demonstrate PB approaches (example figures)
  • S0: script to create png files to aid in author's diagonstics of the implimented framework

The above ordering (with integer ordering within each subsection) can be used to completely replicate our analysis. More specifically we provide a makefile to add in the replication of our work (see (following subsection)[#11-makefile-usage]).

1.1 makefile usage

To run the full analysis we provide a makefile containing recipes to preform all final analysis and reproduce tables and figures for the paper.

Note: would take multiple days to run if you do the full analysis without "psuedo-parallelization". See notes in the following list.

  • make all: runs the full analysis

  • make create_data: download, clean and create raw data for analysis

  • make simulate_tcs: create simulated curves for PB creation and PB validation analysis

    For this complete process (make simulate_tcs) it took around 43 hours to run on a server (specs). It takes 29 hours to run the creation of 350 simulated paths for each TC (309) of all simulation types using the following line of code:

    Rscript main/2-simulate_test_paths.R
  • make create_pbs: analysis to create PBs

    Within the creation of the pbs (completed in this make command) we break up the process into blocks making PBs for 25 TCs in each block. A single block takes around 23 hours. When we ran the code we broke up the analysis on multiple servers with multiple blocks running at the same time (with similar to those mentioned here). One should expect around 276 hours of processing time for this step (excluding the recombining of

    all the PBs into 1 location with

    Rscript main/4.1-collect_parallel_data_files.R output_pipeline_alphalevel0.1 output_pipeline _all

    which is the final call in make create_pbs). See comments in (Special script guidelines)[#12-special-script-guidelines] for running of either file associated with this section (i.e. 4-prediction_band_pipeline.R or 4.1-collect_parallel_data_files.R).

  • make validate_pbs: validation analysis for PBs This call runs the validation script 5-simulation_validation_pipeline.R twice for the sets of (100) and (75) new simulated curves. See special script guidelines if you'd like to run the analysis yourself.

  • make create_figs: create figures and tables for paper

  • make create_diagrams: create diagrams for the paper

Server Specs

We ran this analysis on a Statistics and Data Science department server (lerna) with the following specs:

Intel(R) Xeon(R) CPU X5680 @ 3.33GHz

1.2 Special Script Guidelines

Two of the files in the main/ folder, 4-prediction_band_pipeline.R and 4.1-collect_parallel_data_files.R, require input parameters of the following structure:

1.2.1 4-prediction_band_pipeline.R

To run the 4-prediction_band_pipeline.R we would do the following from the TC-prediction-bands folder:

Rscript main/4-prediction_band_pipeline.R a b

where a and b are integer values (a <= b). This call will create PBs for TCs in the testing data with indices a to b and store them in an .Rdata file labeled 'output_pipeline_alphalevel0.1_[a]_[b].Rdata (with the [a] subsituted for the string represented of the integer a). This allows of the creation of the PB objects to be created in "parallel".

1.2.2 4.1-collect_parallel_data_files.R

To run 4.1-collect_parallel_data_files.R we would run the following from the TC-prediction-bands folder:

Rscript main/4.1-collect_parallel_data_files.R a b c


  • a is the string that all files to merge should have at the beginning. Note 1: if you files with the same beginning that you don't wish to merger you'll need to change there names (e.g. output_pipeline_alphalevel0.1). Note 2: this file already assumes the location of the data files to merger is main/data.

  • b the variable name you'd like to save the final list object as (e.g. output_pipeline).

  • c the final part the string for the .Rdata file that will save the object named b, this needs to exclude .Rdata in the end. (e.g. _all or _all_2018-07-02)

If we followed all the "e.g"s then we would be saving a object called output_pipeline to the file


1.2.3. 5-simulation_validation_pipeline.R

To run 5-simulation_validation_pipeline.R we would run the following from the TC-prediction-bands folder:

Rscript 5-simulation_validation_pipeline.R a


  • a is the string for the file name in main/data/ folder that contains an environment that contains lists of simulated curves for the test curves.

Note that we save the output in a file called sim_validation_resultsXX.Rdata where XX is the number of simulated curves for each test curve.

2 TCpredictionbands package

To install the latest version please do

devtools::install_github(repo = 'Mr8ND/TC-prediction-bands/TCpredictionbands')


This project started in Carnegie Mellon University's 10-701: Introduction to Machine Learning in the Fall of 2016. After this class, we have worked with support from Professor Chad Schafer.

4 Contributors

This repository is public and owned by Nic Dalmasso, Robin Dunn and Benjamin LeRoy.

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