Daily Demand Forecasting Orders
Donated on 11/20/2017
The dataset was collected during 60 days, this is a real database of a brazilian logistics company.
Dataset Characteristics
Time-Series
Subject Area
Business
Associated Tasks
Regression
Feature Type
Integer
# Instances
60
# Features
12
Dataset Information
Additional Information
The database was collected during 60 days, this is a real database of a Brazilian company of large logistics. Twelve predictive attributes and a target that is the total of orders for daily. treatment
Has Missing Values?
No
Introductory Paper
By R. P. Ferreira, Andréa Martiniano, Arthur Arruda Leal Ferreira, Aleister Ferreira, R. Sassi. 2016
Published in IEEE Latin America Transactions
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| Week of the month | Feature | Integer | (first, second, third, fourth or fifth week) | no | |
| Day of the week | Feature | Integer | (Monday to Friday) | no | |
| Non-urgent order | Feature | Continuous | no | ||
| Urgent order | Feature | Continuous | no | ||
| Order type A | Feature | Continuous | no | ||
| Order type B | Feature | Continuous | no | ||
| Order type C | Feature | Continuous | no | ||
| Fiscal sector orders | Feature | Continuous | no | ||
| Orders from the traffic controller sector | Feature | Integer | no | ||
| Banking orders (1) | Feature | Integer | no |
0 to 10 of 13
Additional Variable Information
The dataset was collected during 60 days, this is a real database of a brazilian logistics company. The dataset has twelve predictive attributes and a target that is the total of orders for daily treatment. The database was used in academic research at the Universidade Nove de Julho. .arff header for Weka: @relation Daily_Demand_Forecasting_Orders @attribute Week_of_the_month {1.0, 2.0, 3.0, 4.0, 5.0} @attribute Day_of_the_week_(Monday_to_Friday) {2.0, 3.0, 4.0, 5.0, 6.0} @attribute Non_urgent_order integer @attribute Urgent_order integer @attribute Order_type_A integer @attribute Order_type_B integer @attribute Order_type_C integer @attribute Fiscal_sector_orders integer @attribute Orders_from_the_traffic_controller_sector integer @attribute Banking_orders_(1) integer @attribute Banking_orders_(2) integer @attribute Banking_orders_(3) integer @attribute Target_(Total_orders) integer @data
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset daily_demand_forecasting_orders = fetch_ucirepo(id=409) # data (as pandas dataframes) X = daily_demand_forecasting_orders.data.features y = daily_demand_forecasting_orders.data.targets # metadata print(daily_demand_forecasting_orders.metadata) # variable information print(daily_demand_forecasting_orders.variables)
Ferreira,Ricardo, Martiniano,Andrea, Ferreira,Arthur, Ferreira,Aleister, and Sassi,Renato. (2017). Daily Demand Forecasting Orders. UCI Machine Learning Repository. https://doi.org/10.24432/C5BC8T.
@misc{misc_daily_demand_forecasting_orders_409,
author = {Ferreira,Ricardo, Martiniano,Andrea, Ferreira,Arthur, Ferreira,Aleister, and Sassi,Renato},
title = {{Daily Demand Forecasting Orders}},
year = {2017},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5BC8T}
}Creators
Ricardo Ferreira
Andrea Martiniano
Arthur Ferreira
Aleister Ferreira
Renato Sassi
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.