DataRobot APIのリソース > API user guide > Common use cases > Python v2.x use cases > Forecast sales with multiseries modeling

Forecast sales with multiseries modeling¶

The use case provided in this notebook forecasts future sales for multiple stores using multiseries modeling. Multiseries modeling allows you to model datasets that contain multiple time series based on a common set of input features. In other words, a dataset that could be thought of as consisting of multiple individual time-series datasets with one column of labels indicating which series each row belongs to. This column is known as the series ID column.

Multiseries is useful for large chain businesses that want to create a forecast to correctly order inventory and staff stores with the needed number of people for the predicted store volume. An analyst managing the stores uses DataRobot to build time series models that predict daily sales.

Import libraries¶

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import datetime as dt
from datetime import datetime
from importlib import reload
import os
import re

import datarobot as dr
from datarobot import Deployment, Project
import dateutil.parser
import numpy as np
import pandas as pd
from pandas.io.json import json_normalize
import datetime as dt
from datetime import datetime
from importlib import reload
import os
import re

import datarobot as dr
from datarobot import Deployment, Project
import dateutil.parser
import numpy as np
import pandas as pd
from pandas.io.json import json_normalize

Connect to DataRobot¶

Read more about different options for connecting to DataRobot from the client.

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# If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call
# dr.Client(config_path='path-to-drconfig.yaml')
# If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call
# dr.Client(config_path='path-to-drconfig.yaml')

Create a dataframe¶

Use the snippet below to display your dataset as a dataframe. You can download the sample dataset used below here.

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data_path = "https://docs.datarobot.com/en/docs/api/guide/common-case/DR_Demo_Sales_Multiseries_training.csv"

df = pd.read_csv(data_path, infer_datetime_format=True, parse_dates=["Date"], engine="c")

df.head(5)
data_path = "https://docs.datarobot.com/en/docs/api/guide/common-case/DR_Demo_Sales_Multiseries_training.csv"

df = pd.read_csv(data_path, infer_datetime_format=True, parse_dates=["Date"], engine="c")

df.head(5)

Out[3]:

	Store	Date	Sales	Store_Size	Num_Employees	Num_Customers	Returns_Pct	Pct_On_Sale	Pct_Promotional	Marketing	TouristEvent	Econ_ChangeGDP	EconJobsChange	AnnualizedCPI
0	Louisville	2012-07-01	109673	20100	68	531	1.03	9.96	0.000047	July In Store Credit Card Signup Discount; In ...	No	0.5	NaN	0.02
1	Louisville	2012-07-02	131791	20100	34	476	0.41	8.65	0.000047	July In Store Credit Card Signup Discount; In ...	No	NaN	NaN	NaN
2	Louisville	2012-07-03	134711	20100	42	578	0.31	8.96	0.000047	July In Store Credit Card Signup Discount; In ...	No	NaN	NaN	NaN
3	Louisville	2012-07-04	97640	20100	54	569	0.83	10.08	0.000047	July In Store Credit Card Signup Discount; In ...	No	NaN	NaN	NaN
4	Louisville	2012-07-05	129538	20100	62	486	0.51	9.80	0.000047	July In Store Credit Card Signup Discount; ID5...	No	NaN	NaN	NaN

Plot the sales of each store¶

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df.pivot(index="Date", columns="Store", values="Sales").plot(figsize=(18, 8));
df.pivot(index="Date", columns="Store", values="Sales").plot(figsize=(18, 8));