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Machine learning model selection for Time Series problem ?


Hi All,

I am trying to apply machine learning algorithm to a dataset which consits of emission of pollutant gas from an engine called SO2(target variable) which is collected over 6 months of time for at a interval of each of 15 mins each.The dataset also do have other independent variables like pressure,vapour etc with time.
Now the question is
should i go for time series modelling like arima for forcasting the So2?
or should i go for randomforest or svm for forecasting?

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Black Belt

Rajgure, Akash , regressions like arima are attractive for time series modelling, but it's seems to me that it's better to use Python's Keras / TensorFlow library, specifically RNNs, LSTM and GRU for time series forecasting.