Skip to content
Predicting PWV values using exponential smoothing
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
results
.gitignore
README.md
benchmarking.py
grid_search.py
pwv_forecasting.py
read_matfile.py

README.md

Predicting GPS-based PWV Measurements Using Exponential Smoothing

With the spirit of reproducible research, this repository contains all the codes required to produce the results in the manuscript:

S. Manandhar*, S. Dev*, Y. H. Lee and S. Winkler, Predicting GPS-based PWV Measurements Using Exponential Smoothing, IEEE AP-S Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019 (* Authors contributed equally).

summary

Please cite the above paper if you intend to use whole/part of the code. This code is only for academic and research purposes.

Code organization

The codes are written in python. The codes are tested in python3 version.

Dependencies

  • matplotlib: pip3 install matplotlib
  • Tkinter: sudo apt-get install python3-tk
  • statsmodels: pip3 install statsmodels
  • scipy: pip3 install scipy

Usage

  1. python3 pwv_forecasting.py: Run this script to compute the predicted PWV values, based on the historical PWV data. This generated Figure 1 of the paper. You need to change the value of the parameter start_index in the file, to check for different slices of time-series data.
  2. python3 grid_search.py: Run this script to obtain the distribution of RMSE values w.r.t. historical data and lead times. This generates Figure 2 of the paper. It saves the figure as ./results/rmse.pdf and the corresponding numpy array as rmse_matrix_for_grid.npy.
  3. python3 benchmarking.py: Run this script to obtain the benchmarking results of the different methods. This generates the results in Table 1 of the paper. The results are stored in ./results/comparison.txt file.

Scripts

  • benchmarking.py: For a particular past time observation, we compare the performance of various methods for various lead times, and store in a text file.
  • grid_search.py: Performs the experiments for various lead times and various historical observations. This is repeated for several times, and the average is reported.
  • pwv_forecasting.py: Performs pwv forecasting for sample datapoints and plots the figure with train and test legends
  • read_matfile.py: Reads the matlab files, performs pre-processing and returns the data series
You can’t perform that action at this time.