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Home Page: http://bikeshare.dssg.io/
Statistical models and webapp for predicting when bikeshare stations will be empty or full.
Home Page: http://bikeshare.dssg.io/
Just to make things neater!
Once we have the model we want, we need to estimate its parameters by crunching historical data of number of bikes and docks at each station.
So we need a parameter_estimation.py
script that:
Make sure that requirements.txt contains every package for the project.
parameters should include things like:
-model being validated
-number of time points into the future to predict
-minimum number of time points with which to fit the model
Here are the fields we want to show in a table on a page:
This involves figuring out the best way to use some data as a test set. For example, do we randomly leave out data points and subsequent data points (based on the "degree" of the AR model we're using), train the model, and then see how we did on our left out points?
Homepage
Intro: have a sentence or two about the project: the problems its solving, the partner, and how you're solving it. Also say that "this wiki is the central place to learn about the social problem we worked on, the data we used, the methods we used to solve it, and our findings" so people know what they're looking at.
List of pages in the wiki
Problem
An in-depth description of the problem your organization, the problem you're trying to solve, and any relevant domain knowledge. Feel free to copy from blog posts and posters, if relevant.
Data
Describe the dataset(s) you used in the project as well as your database. Walk people through the data model (tables are handy for this), and include a (fake) sample of each dataset.
If you scraped data, this is the place to document that.
Methodology
An in-depth, technical write up of the method(s) you used on your projects. Use latex equation, walk people through algorithms and models, link out to relevant documentation when possible.
Results
Discuss what metrics you're using to evaluate performance (if applicable), and what your final findings where.
Future work
Discuss what you would like to do / what is in progress.
Analysis
If you did exploratory data analysis, this is the place to put it and explain your findings. Explain each finding and what your learned from it / how it motivated the methods you used. Put this between the "Data" and "Methodology" sections. Feel free to lift content from relevant blog posts, if any.
Resources
Resources for domain knowledge, methods, and tech. Whatever pieces of paper you used to learn what you know.
Tool
We're using an ARMA statistical model to predict the number of bikes and docks available at a station at a certain time.
They can be more or less complicated, so let's figure out what terms we want in the first, simple version. We can also do something more complicated, but let's start simple.
so we can readd models.py
We'll need to distinguish between morning and evening rush hours
Once we've picked a statistical model and estimated its parameters, we need to use this model to actually predict how many bikes are going to be at every station in a bikeshare system in 60 minutes.
So we need a ARMA_prediction.py
script that
parameter_estimation.py
and uses them for our modelMaybe using Mapbox.js
By scraping the JSON/XML endpoints each minute, we can 'live-update' the database.
Train from Feb 2012 to Feb 2013, then predict out 15-60 min with a day + hour offset.
Turn the data into CSVs.
*V1 [XML] Data
*V2 [JSON] Data
update model validation script and glm_model script to include function
webapp
, database
, and models
.data
or database
folder, provide a way to re-create your database from scratch. .sql
files are often appropriate for this.Built and implement an install script so people can analyze locally
using SQLAlchemy
For those you you using scipy/numpy etc, add proper notes in requirements.txt
An important input into our prediction script is the current number of bikes at each station.
Currently we only have historical bike availability data in the database. So we need to write a scraper that hits the API for DC and adds current station status to the database.
add hourly temp and perception intensity
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