A library for Time-Series exploration, analysis & modelling.
Project description
tseuler
A library for Time Series exploration, analysis & modelling. This includes -
As of now, this libray is in pre-alpha phase, i.e there is a lot of work still left before its first stable release.
TSMAD - Time Series Mini Analysis DashBoard.
Functionalities Include
- A mini Dashboard for Time Series Analysis, with multiple variations to each kind of analysis
- Inbuilt Freqency Variation analysis
- Intervention Analysis (In Future)
TSSTATS - Time Series Statistical & Modelling Functions
Functionalities Include:
- Rolling Origin Framework (Currently Supports - statsmodels, sklearn, sklearn) for both multi-variate and uni-variate
- Residual Diagnostics
- Statistical Tests
- Entropy Calculations
- Intervention Analysis (In Future)
Example
Installation
Installation
pip install tseuler
Usage
-
Instantiating a DashBoard
import pandas as pd import tseuler as tse # Read the Time Series DataFrame dataDF = pd.read_csv('Raw Data/stocks_data.csv', index_col=0) tsmadObj = tse.TSMAD(tsdata = dataDF, data_desc = 'Stocks Data', target_columns = ['close'], categorical_columns = ['Name'], dt_format = '%Y-%m-%d', dt_freq = 'B', how_aggregate = {'open':'first', 'high':'max', 'low':'min', 'close':'last'}, force_interactive = True) tsmadObj.get_board()
tseuler
has been built upon:-
- pandas
- numpy
- panel
- altair
- matplotlib
- statsmodels
History
v0.0.4dev0 : Development Package
- Added TSMAD
- Added TSSTATS
Project details
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