CODEWITHSUNDEEP
pythonmachine-learningscikit-learndeep-learningdata-science
Nikita Belov
Nikita Belov

Reputation: 85

Need approach recomendation in field ML or DL in forecast value task

I am trying to forecast value in 30 days. I have a timeseries data with some parameteers. The example of date I will attach at the bottom.

The main idea is that Y value is our aim variable to predict in 30 days from today. The f1-f5 variables is values which influence on Y value. So I need to predict Y using Date and f1-f5 columns. All the data comes every day.

Recomend me please some ML and DL approaches to predict "Y" value?

My thoughts. As I understood it is time series data. And the task is regression. But I am a bit disapointed because time series approaches, as I understood, predict value based on only date value, using seasonality and so on. But I afraid that if I will use XGBoost or Linear regression approaches I will loose timeseries effect on this data.

Date,f1,f2,f3,f4,f5,Y
2015-01-01,183,34,15,1166,50,3251
2015-01-02,364,173,5,739,32,8132
2015-01-03,83,72,38,551,49,6271
2015-01-04,183,81,7,937,32,3334
2015-01-05,324,61,73,554,71,3742
2015-01-06,183,97,15,337,17,5543
2015-01-07,38,152,83,883,32,9143
2015-01-08,78,72,5,551,11,6435
2015-01-09,183,30,21,443,92,4353
...,...,...,...,...,...,...
2018-06-08,924,9,53,897,88,7446

Upvotes: 1

Views: 85

Answers (1)

Maxim Volgin
Maxim Volgin

Reputation: 4077

Time series are traditionally modeled with AR (auto-regression) and MA (moving average). Trend and seasonality should also be accounted for. So why not use ARIMA or Prophet? Here's some theory on the subject - https://otexts.com/fpp2/

There are some ML/DL implementations based on RNN/LSTM but they are really complex, often hard to explain, and tend to suffer from vanishing gradient problem. If you must use ML/DL, you may want to have a look at LSTNet.

Upvotes: 1

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