forecasting
Forecasting materials for Making a difference with health data
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 8 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (9.1%) to scientific vocabulary
Repository
Forecasting materials for Making a difference with health data
Basic Info
- Host: GitHub
- Owner: health-data-science-OR
- License: mit
- Language: Jupyter Notebook
- Default Branch: master
- Size: 28 MB
Statistics
- Stars: 6
- Watchers: 1
- Forks: 3
- Open Issues: 4
- Releases: 11
Metadata Files
README.md
HPDM097: Making a difference with health data:
Forecasting health service demand
Forecasting practical materials for Making a difference with health data module.
Dependencies
Please use the provided conda environment
conda env create -f binder/environment.yml
conda activate hds_forecast
Citation:
Monks, T. (2023). forecasting health service demand in python. Zenodo. https://doi.org/10.5281/zenodo.4332600
tex
@software{monks_2023_10370697,
author = {Monks, Thomas},
title = {forecasting health service demand in python},
month = dec,
year = 2023,
publisher = {Zenodo},
doi = {10.5281/zenodo.4332600},
url = {https://doi.org/10.5281/zenodo.4332600}
}
Syllabus
RECOMMENDED Pre-course material
These notebooks offer a refresher in the basics of date handling in numpy, pandas and matplotlib.
Handling dates in numpy and pandas:
Exploring time series with pandas and matplotlib:
Computer Lab 1: The basics of forecasting: part 1
1.1 Code along notebooks
These notebooks accompany the exercises. They provide example code to help you solve the exercises.
Loading time series data into pandas:
Naive benchmarks:
1.2 Exercises
- Naive forecasting exercises
Computer Lab 2: The basics of forecasting: part 2
2.1 Code along notebooks
- Introduction to cross validation
2.2 Exercises
- Time series cross validation
Computer Lab 3: Forecasting using ARIMA models
3.1 Code along notebooks
- Introduction to ARIMA Exercises:
3.2 Exercises
- ARIMA Exercise:
Computer Lab 4: Forecasting daily data using Facebook Prophet
4.1 Code along notebooks
Prophet introductory lecture:
Introduction to Prophet Exercises:
4.2 Exercises
- Prophet Exercises:
Computer Labs 5. An introduction to feedforward neural networks
5.1. code along lecture notebooks
- Deep Learning 101:
5.2 Exercises
- Autoregressive Neural Networks with KERAS. Part 1:
5.3. Optional self study material
Preprocessing and autoregressive OLS:
Autoregressive Neural Networks PYTORCH. Part 1:
Computer Lab 6: Feedforward neural networks for time series
6.1 Exercises
- Autoregressive Neural Networks KERAS Part 2:
6.2 Optional self study material
- Autoregressive Neural Networks PYTORCH Part 2:
Owner
- Name: Health Data Science and Operations Research
- Login: health-data-science-OR
- Kind: organization
- Repositories: 14
- Profile: https://github.com/health-data-science-OR
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: forecasting health service demand in python
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Thomas
family-names: Monks
affiliation: University of Exeter
orcid: 'https://orcid.org/0000-0003-2631-4481'
repository-code: 'https://github.com/health-data-science-OR/forecasting/'
keywords:
- forecasting
- python
- open science
license: MIT
GitHub Events
Total
- Create event: 1
- Release event: 1
- Issues event: 1
Last Year
- Create event: 1
- Release event: 1
- Issues event: 1
Dependencies
- forecast-tools ==0.2.1
- pmdarima ==2.0.4
- prophet ==1.1.5
- scikit-learn ==1.3.2
- tensorflow-cpu ==2.14.0