Orange3-Timeseries
Orange add-on for analyzing, visualizing, manipulating, and forecasting time series data. The add-on includes ARIMA and VAR models, model evaluation, time series preprocessing, seasonal adjustment and a wide array of visualizations. See documentation.
Features
Use time series data
- reinterpret data as time series
- induce missing values
- generate time series from Yahoo Finance stock market data
Analysis of time series data
- aggregate data by a given time interval
- decompose the time series into seasonal, trend, and residual components
- apply rolling window functions to the time series
- make forecasts for the future
- evaluate models
Visualize time series data
- visualize time series’ sequence and progression
- visualize variables’ auto-correlation
- visualize time series’ cycles, seasonality, periodicity, and most significant periods
8 maintainers
Metadata
8 maintainers
Release files for Orange3-Timeseries 0.6.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| Orange3-Timeseries-0.6.3.tar.gz | 2.3 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| Orange3_Timeseries-0.6.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.5 MB
Release files / Orange3-Timeseries-0.6.3.tar.gz
| Download URL | Orange3-Timeseries-0.6.3.tar.gz |
|---|---|
| Size | 2.3 MB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.12.2
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Release files / Orange3_Timeseries-0.6.3-py3-none-any.whl
| Download URL | Orange3_Timeseries-0.6.3-py3-none-any.whl |
|---|---|
| Size | 280.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.12.2
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