There is a code example that gives to increase the amount of series data by adding slightly modified copies of already existing time series data or newly created synthetic series data from existing data?

From the following code,

from timeseries_generator import HolidayFactor, LinearTrend, Generator

lt = LinearTrend(coef=2.0, offset=1., col_name="my_linear_trend")

g: Generator = Generator(factors={lt}, features=None, date_range=pd.date_range(start="01-01-2020", end="01-01-2021"))

holiday_factor = HolidayFactor(
    country_feature_name="country",
)
g.add_factor(holiday_factor)
g.generate()

I get the error. I am not sure this is expected behavior.

File /usr/local/Caskroom/miniconda/base/envs/tf/lib/python3.9/site-packages/pandas/core/frame.py:10083, in DataFrame.merge(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
...
-> 1849     raise KeyError(key)
   1851 # Check for duplicates
   1852 if values.ndim > 1:

KeyError: 'country'

Hi team, Thanks for the useful library! I wonder if you'd be open to this idea:

I would like to be able to:

• Set up categorizing features (let's say, for illustration, CATEGORY=[footwear, t-shirts, socks], SIZE=[S, M, L, US-Mens-8, US-Womens-6) and define Factors on them
• Generate time-series with more restricted feature combinations than the outer product (again for illustration, "t-shirt sizes for t-shirts, shoe sizes for footwear")

Today, it seems like Generator.generate() hard-codes the assumption that time-series should be generated for the product of all provided feature values.

It'd be helpful if, instead, we could have the option of customizing this join to limit down generated combinations?

Some options I can think of:

1. Leave the library as-is: Users generate full outer product and limit down what they want in post-processing
   • This seems possible already, but very RAM-intensive if your desired combinations are sparse?
2. Accept an optional dataframe of factor combinations as parameter to the generate() method
   • Gives full flexibility over which combinations are kept / ignored, without assuming any particular rigid hierarchies between features
   • ...But might need to do a bit of validation to protect against user errors? May not be super easy to use without some documented examples / functions to generate the dataframe
3. Some more complex API for feature configuration that accommodates specifying valid/invalid feature combinations
   • Might be nicer for usability, but difficult to make general: E.g. a straightforward hierarchy could be represented as a nested dict, but in practice many applications have multiple intersecting views of product category information e.g. brand, type, target segment, etc.

First of all, thank you for making this repository public! I enjoy its ease of use and the built-in factors.

Problem description

I'm currently trying to generate revenue data for a bar/restaurant on an hourly basis. As far as I can see, the timeseries-generator only supports generating one data point per day, not per hour.

I tried to generate hourly data like g = Generator(factors={lt}, features=None, date_range=pd.date_range(start='15/9/2021', end='30/9/2021', freq='h')) which didn't work.

Potential solution

Add the possibility to generate hourly data too. If this is a promising idea in your opinion, I'm willing to contribute to the implementation.

Thank you in advance!