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I have 1M+ multivariate items that I need to forecast. Each item has 3 features. The data looks like this:
Month
Item
Feat A
Feat B
Feat C
Jan
X
10
0.5
0
Feb
X
12
0.75
0
Mar
X
15
0.25
0.01
Apr
X
13
1
0
I'd like to try modeling the features in a univariate layout because while I don't think there are dependencies between the features within an item, there are general learning we can make about features A, B and C.
Month
Item
Feature
Y
Jan
X
A
10
Feb
X
A
12
Mar
X
A
13
Apr
X
A
15
Jan
X
B
0.5
Feb
X
B
0.75
Mar
X
B
0.25
Apr
X
B
1
Jan
X
C
0
Feb
X
C
0
Mar
X
C
0.01
Apr
X
C
0
How can I load and forecast my data in this layout in gluonts? Item ID is required to be a string and not multiple columns, so I'd be forced to do item_feature which obscures the point of including feature in the first place.
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I have 1M+ multivariate items that I need to forecast. Each item has 3 features. The data looks like this:
I'd like to try modeling the features in a univariate layout because while I don't think there are dependencies between the features within an item, there are general learning we can make about features A, B and C.
How can I load and forecast my data in this layout in gluonts?
Item ID
is required to be a string and not multiple columns, so I'd be forced to doitem_feature
which obscures the point of including feature in the first place.Beta Was this translation helpful? Give feedback.
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