Prediction/Forecastig of one variable with relation of multiple features My dataset is composed of  time series (40 points) with multiple variables
                    A      B       C       D     ...   Target
    Release Date                                            
    2022-06-01    0.008   15490   69600   16950  ...  1.044659
    2022-05-01    0.007   14500   78920   19874  ...  1.035948
        ...

My goal is to forecast the target value with the relation of all the features combined.
I tried to predict the last column with a simple linear regression, but I got a fluctuating score with each run.
Is it more correct to treat this problem as a Multivariate Time Series / Temporal Convolutional Neural Network?

In Multivariate time series Each variable depends not only on its past values but also has some dependency on other variables. This dependency is used for forecasting future values, So if A, B, C ,... have a relation with each others so you have to use Multivariate time series.
To check if they have a relation or not you can plot the heatmap and see the correlation between the features.
Ex: if you live in country that use a currency depend on dolar when you want to predict the price of any thing you have to add dolar values because every thing depend on dolar.
Date     Egg-Price-currency     Dolar-price
01-06         500                   10
01-07         600                   12
01-09         ???                    9

If you want to predict the egg price in 01-08 without using Dolar-price this will be wrong (the pattern is always up without dollar price but dolar price went down so the price will go down)
I hope i explained this clearly.
