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Revenue Projection


How to deal with time series which change in seasonality or other patterns?How to merge monthly, daily and weekly data?Prediction approach on unique data or progressive dataHow do I find a repeating pattern of unknown length and start within a stringWhat to give as predictors to predict future values?How to predict next year's gross revenue given this year's data?What model can I build with a limited dataset?Why does balancing the test dataset improve precision-recall curve?model to predict annual outcome based on previous years data













0












$begingroup$


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?






time-series predictive-modeling







share|improve this question











$endgroup$











  • $begingroup$
    A simple linear regression will suffice for this
    $endgroup$
    – Gaius
    yesterday















0












$begingroup$


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?






time-series predictive-modeling







share|improve this question











$endgroup$











  • $begingroup$
    A simple linear regression will suffice for this
    $endgroup$
    – Gaius
    yesterday













0












0








0





$begingroup$


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?






time-series predictive-modeling







share|improve this question











$endgroup$




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?






time-series predictive-modeling




time-series predictive-modeling






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited yesterday









I_Play_With_Data

1,212531




1,212531










asked yesterday









william007william007

26919




26919











  • $begingroup$
    A simple linear regression will suffice for this
    $endgroup$
    – Gaius
    yesterday
















  • $begingroup$
    A simple linear regression will suffice for this
    $endgroup$
    – Gaius
    yesterday















$begingroup$
A simple linear regression will suffice for this
$endgroup$
– Gaius
yesterday




$begingroup$
A simple linear regression will suffice for this
$endgroup$
– Gaius
yesterday










1 Answer
1






active

oldest

votes


















0












$begingroup$

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






share|improve this answer









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    1 Answer
    1






    active

    oldest

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    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    0












    $begingroup$

    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






    share|improve this answer









    $endgroup$

















      0












      $begingroup$

      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






      share|improve this answer









      $endgroup$















        0












        0








        0





        $begingroup$

        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






        share|improve this answer









        $endgroup$



        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







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered yesterday









        PreetPreet

        3234




        3234



























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