Python framework to manage time series.
Project description
OUTATIME
Python Framework to Manage Time Series.
Overview
The main goal of this framework is to simplify the collection of temporal data and related operations. It is based on the concepts of TimeSeries, as a collection of records associated with a given day, and Granularity, to indicate a given time interval (e.g. daily, weekly, monthly, etc.).
The object related to a single day (TimeSeriesData) contains two attributes:
- day - the reference date for that record
- data - an object that collects any information for that day
On the time series it is possible to carry out numerous operations of different types, as for example:
- add or remove records
- search records by date
- exclude records outside a determined range
- resampling of data
- union and intersection
- batch splitting and data aggregation
- other
Installation
pip install outatime
Framework Structure
outatime
├── dataclass
│ └── time_series_data.py --> Class used to manage daily data.
│
├── granularity
│ ├── granularity.py --> Set of classes used for managing time intervals of different length.
│ ├── granularity_factory.py --> Factory class for creating granularity objects.
│ └── utils.py --> Utils related to granularities.
│
├── timeseries
│ ├── batches.py --> Set of methods to operate on time series dividing them into batches.
│ ├── expr.py --> Set of operations between time series.
│ └── time_series.py --> Core class that represents a series of daily records.
│
└── util
├── agenda.py --> Utils related to calendar info and evalutations.
├── bisect.py --> Utils related to binary search.
├── decorators.py --> Useful decorators.
└── relativedelta.py --> Class that extends relativedelta with useful properties.
License
MIT license, see LICENSE
file.
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