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Given that we have

  1. Monthly revenue data for pass 3 years (36 rows of revenue)
  2. We have other data including economic indicators, industry indicators as well (other columns in the 36 rows)

What models and approaches are suitable in projecting next month revenue (say April) in this case?

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  • $\begingroup$ A simple linear regression will suffice for this $\endgroup$ – Gaius Mar 15 '19 at 10:57
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You have got yourself a time series forecasting problem. And with multiple input variables it is called multivariate time series forecasting.

What Is Time Series Forecasting?

You can start with EDA on your data and find out if you can see any trend or seasonality. ( You might need to add or update your current features to get underlying trend/seasonality )

After EDA, you can start looking into following models, all of them are the go-to for time series prediction problem:

  • Classical, Statistical
    • ARMA for stationary data
    • ARIMA for data with a trend - Refer
    • SARIMA for data with seasonality
    • Holt-Winters Forecasting - Refer
    • Theta method - Refer
    • Fourier Transformation - Refer
  • Machine Learning
    • Quantile Regression Forest(QRF)
    • Support Vector Regression(SVR)
    • Recurrent Neural Networks(RNNs) (LSTM)

If you are not comfortable with Statistics then I would advise you to start with LSTMs for forecasting - Refer

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