Closed Sandy4321 closed 2 years ago
Hello @Sandy4321, sorry for the late reply I'm very sorry to inform you that, currently this is only support for one variable time series data The data used in this project is very limited due to the "data cleaning" process is build from investing.com (my main time series data source for this project) I'm building this project mainly for my educational purpose only As of now, personally i highly recommend using python for multi variative data FTS, because as you may already know, python is very powerful language for data analytics and data visualization. The algorithm in this project is based on Jens Runi Poulsen Proposed FTS Method If you're interested to further developing this project, feel free to contact me again :)
Jens Runi Poulsen Proposed FTS Method
Yes it is interesting May you share links to git hub code or any other material pls
On Fri, Jan 22, 2021, 10:18 AM Syarif Ilyasha @.***> wrote:
Hello @Sandy4321 https://github.com/Sandy4321, sorry for the late reply I'm very sorry to inform you that, currently this is only support for one variable time series data The data used in this project is very limited due to the "data cleaning" process is build from investing.com (my main time series data source for this project) I'm building this project mainly for my educational purpose only As of now, personally i highly recommend using python for multi variative data FTS, because as you may already know, python is very powerful language for data analytics and data visualization. The algorithm in this project is based on Jens Runi Poulsen Proposed FTS Method If you're interested to further developing this project, feel free to contact me again :)
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great code thanks may you clarify : will it work for multivariate time series prediction both regression and classification 1 where all values are continues values 2 or even will it work for multivariate time series where values are mixture of continues and categorical values for example 2 dimensions have continues values and 3 dimensions are categorical values
1 black 56 m 160 34 2 white 77 f 170 54 3 yellow 87 m 167 43 4 white 55 m 198 72 5 white 88 f 176 32