My understanding is that Tabular Model works with 2 dimensions (rows + columns) tables as input dataset.
-> What would be the best way apply the model for multiple (more then 2) input dimensions?
Here the details:
My input data is a table (target is Price)
But I believe that in my domain, today’s price might be very dependent from the prev days prices.
So I need this to put as input the previous n prices (in this case n = 3)
And this for all the types, so I need an other dimension:
Therefore the model can predict daily prices for all types using as input all the prev prices for all types.
-> Do you have any nice ideas how to achieve this with Tabular or with other Models?
-> Pandas DataFrame can have only 2 axis, so I am not able to build this 3d table using DataFrames. Any suggestions?
You should represent your data as a multivariate time series. These are some repos to deal with TS in fastai:
Thanks for the input Victor.
I am using fastai v1 at the moment, so if I understood correctly only this package is compatible with fastai v1 at the moment.
which links to this:
Maybe it’s a quite basic question, but since I am quite new with notebooks, do you have any tips on how can I install the package?
I tried this, but it does not work ( I am using Google Collab)
!pip install git+https://github.com/timeseriesAI/timeseriesAI1/tree/master/fastai_timeseries#egg=timeseriesAI
Basically I have an error while importing fastai_timeseries
from fastai_timeseries import *
in this notebook:
Thanks in advance
git clone it instead of
pip install it. You should also install (this time via
pyunpack in order to make it work.