M5_CA1_basefc | R Documentation |
This dataset contains forecasts for the hierarchy of time series related to the store CA_1
from the M5 competition.
M5_CA1_basefc
A list containing:
upper
: a list of 11 elements each representing an aggregation level. Each element contains: mu
, sigma
the mean and standard deviation of the Gaussian forecast, actual
the actual value, residuals
the residuals of the model used to estimate forecasts covariance.
lower
: a list of 3049 elements each representing a forecast for each item. Each element contains pmf
the probability mass function of the item level forecast, actual
the actual value.
A
: the aggregation matrix for A.
S
: the S matrix for the hierarchy.
Q_u
: scaling factors for computing MASE on the upper forecasts.
Q_b
: scaling factors for computing MASE on the bottom forecasts.
The store CA_1
contains 3049 item level time series and 11 aggregate time series:
Store level aggregation (CA_1
)
Category level aggregations (HOBBIES
, HOUSEHOLD
, FOODS
)
Department level aggregations (HOBBIES_1
, HOBBIES_2
, HOUSEHOLD_1
, HOUSEHOLD_2
, FOODS_1
, FOODS_2
, FOODS_3
)
Forecasts are generated with the function forecast
and the model adam
from the package smooth
.
The models for the bottom time series are selected with multiplicative Gamma error term (MNN
);
The models for the upper time series (AXZ
) is selected with Gaussian additive error term, seasonality selected based on information criterion.
The raw data was downloaded with the package m5
.
Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilis. (2020). The M5 Accuracy competition: Results, findings and conclusions. International Journal of Forecasting 38(4) 1346-1364. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ijforecast.2021.10.009")}
Joachimiak K (2022). m5: 'M5 Forecasting' Challenges Data. R package version 0.1.1, https://CRAN.R-project.org/package=m5.
Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilis. (2020). The M5 Accuracy competition: Results, findings and conclusions. International Journal of Forecasting 38(4) 1346-1364. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ijforecast.2021.10.009")}
Svetunkov I (2023). smooth: Forecasting Using State Space Models. R package version 4.0.0, https://CRAN.R-project.org/package=smooth.
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