https://github.com/funinkina/covid19_data-time-series-analysis
Time Series Analysis of Covid-19 Dataset
https://github.com/funinkina/covid19_data-time-series-analysis
arima-forecasting arima-model jupyer-notebook machine-learning prophet-forecasting prophet-model python sarima-model time-series-analysis
Last synced: 3 months ago
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Time Series Analysis of Covid-19 Dataset
- Host: GitHub
- URL: https://github.com/funinkina/covid19_data-time-series-analysis
- Owner: funinkina
- Created: 2024-05-17T18:20:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-17T18:35:58.000Z (about 1 year ago)
- Last Synced: 2024-05-17T19:36:34.693Z (about 1 year ago)
- Topics: arima-forecasting, arima-model, jupyer-notebook, machine-learning, prophet-forecasting, prophet-model, python, sarima-model, time-series-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 1.03 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Time series analysis of Covid 19 date wise dataset
[Here is the dataset](https://www.kaggle.com/datasets/sudalairajkumar/covid19-in-india?select=covid_19_india.csv)
## Models Used:
- Arima
- Sarima
- Prophet
First we made the data stationary and then used SARIMA, ARIMA and Prophet models to determine `Cured`, `Deaths`, `Confirmed` cases for the next 30 days
And used **matplotlib** to visualisation of the data
Also calculated Mean, SE Mean, Standard Deviation, Minimum, Maximum, Skewness and Kurtosis
This is an implementation of this research paper
[Wang Y, Yan Z, Wang D, Yang M, Li Z, Gong X, Wu D, Zhai L, Zhang W, Wang Y. Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models. BMC Infect Dis. 2022 May 25;22(1):495. doi: 10.1186/s12879-022-07472-6. PMID: 35614387; PMCID: PMC9131989.](https://pubmed.ncbi.nlm.nih.gov/35614387/)