Skip to main content

Forecast sales with Entity Embedding LSTM

Project description

AM18_SPR20_LondonLAB

Package Description

The package contains one object Kami with four methods in the order of execution:

  1. Preprocess()
  2. Analyse(n_sample)
  3. Vis()
  4. Forecast(store_list, product_list, start, end)

To initiate the object Kami, the user is required to supply at least these three arguments:

  1. Path to the grouped product sales input data (input_f_path)
  2. Path to an intermediary folder to store intermediary data (cache_dir_path)
  3. Path to an output folder to store final predictions (output_dir_path)

While Preprocess and Vis methods are executed without any argument, Analyse method can be supplied with an optional argument n_sample which is the number of random samples drawn from the predefined training data.

Forecast method is required to be supplied with four arguments including:

  1. A list of stores whose sales are predicted (store_list)
  2. A list of products whose sales are predicted (product_list)
  3. The start date of the forecast (start)
  4. The end date of the forecast (end)

Typical Use Case


from Kami import Kami

obj = Kami(input_f_path = 'PATH_TO_SALES_DATA/SALES_DATA.csv',
		output_dir_path = 'OUTPUT_FOLDER/',
		cache_dir_path = 'CACHE_FOLDER/')
obj.Preprocess()
obj.Analyse()
obj.Vis()
obj.Forecast(store_list = ['STORE_A', 'STORE_B'],
		product_list = ['PRODUCT_A', 'PRODUCT_B'],
		start = 'MM/DD/YYYY',
		end = 'MM/DD/YYYY')

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

Kami-0.4.3.tar.gz (9.9 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page