sbc-classification 0.0.4

Demand Patterns SBC (Syntetos, Boylan, Croston) method of Categorizations

Demand Patterns SBC (Syntetos Boylan Croston) method of Categorizations

Description

This method helps classify different demand patterns (time-series patterns) into groups in order to fit the most appropriate model. More info could be found here and here

This is a Python version of the tsintermittent package in R. At the moment, only the SBC method is available.

Installation

pip install sbc-classification

Example

Test Data

Test data is the clean version of the data from M5 Forecasting Challenge competition on Kaggle. Details can be found here. The dataset was processed to make it easier to test the function.

Testing

Note: This function will not treat NA values. All the null values in the time-series should be treated separately.

from sbc import sbc_class.sbc_class

df = pd.read_csv("./tests/data/sales_train_clean.csv")
## multiple targets
out = sbc_class.(df.iloc[:, 1:], plot_type = 'summary')
## 1 target
out1 = sbc_class.(df.iloc[:, 1], plot_type = 'summary')

print(out)
print(out1)

If plot_type is not None, output would be a plot and a dataframe of target_name, p, CV squared and classified model type for multiple targets

If plot_type is summary then the plot would be a matrix diagram of numbers of time-series in each category

If plot_type is bar then the plot would be bar chart of numbers of time-series in each demand patterns

Please give the package a star if you find it helpful :)