Udacity Capstone Project
This directory contain all code that was used for the Udacity Machine Learning Engineer Nanodegree Program. The folder figures contains all of the pdf figures generated by the codes. The links to the project proposal and the write-up of the final project are below.
- The project proposal: project_proposal.pdf
- The write up of the final project: final_project_write_up.pdf
The enviroments needed to run the program can be installed from the requirements.txt in anaconda using the following command:
$ conda create --name --file requirements.txt
The equations are generated from http://latex.codecogs.com
In this project we have taken data for time series data and we have trained two different neural network models, the feed forward neural network (FFNN) and the long-short term memory (LSTM) network, to generate a time series forecast given the value of the time series at the previous time step. In other words, we have generated a function such that
An example of the forecasts generated by feed forward neural network.
Benchmark ARIMA Results This jupyter notebook contains all of the code used to fit the ARIMA and SARIMA results used for benchmarking
Time Series with FNN This jupyter notebook contains all of the code used to generate the time series results by using the Feed Forward neural network.
Time Series with LSTM This jupyter notebook contains all of the code needed to generate the time series results by using LSTM network architectures.
ARIMA routines This python code contains all of the fitting routines that are used by the code Benchmark_ARIMA_Results.ipynb
The datasets that are included in the directory data directory are the following
- This is the Euro to USD exchange rate data from 2010-2016
- The data set of total number of international airline passengers from 1949-1960, in thousands.
- The number of sun spots observed from 1770-1869.
- The total annual rainfall in London as a function of time from 1813-1912 in inches.