https://github.com/divakarkumarp/time-series-analysis-using-arima

Last synced: 2 months ago
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Host: GitHub
URL: https://github.com/divakarkumarp/time-series-analysis-using-arima
Owner: divakarkumarp
Created: 2022-10-25T12:19:43.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2022-10-25T12:22:54.000Z (over 2 years ago)
Last Synced: 2025-01-22T08:13:34.380Z (4 months ago)
Language: Jupyter Notebook
Size: 296 KB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

### Time-Series-Analysis-using-ARIMA (Autoregressive Integrated Moving Average)
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Time Series Analysis and Forecasting is the process of understanding and exploring time series data to predict or forecast values for any given time interval. This forms the basis for many real-world applications such as sales forecasting, stock market forecasting, weather forecasting and many more.
Not all data that has timestamps or dates as its feature or column can be considered time series data. A time-series data should consist of observations over a regular and continuous interval. Here are some examples of time series data:
Records of observations of the daily stock price from the start of the year to the end of the year. The hourly observation of rising and fall in Bitcoin price over a period of time. Given below is an example dataset that consists of the daily opening and closing price of Bitcoin.

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### Technologies Used:

![](https://forthebadge.com/images/badges/made-with-python.svg)

[](https://numpy.org) [](https://pandas.pydata.org) [](https://seaborn.pydata.org) [](https://matplotlib.org) [](https://jupyter.org/)