Trjtdtk

et qui - how do you really understand that kind of phraseology?

Life insurance that covers only simultaneous/dual deaths

Draw saving money pig (Piggy Bank)

Do I need life insurance if I can cover my own funeral costs?

How do I change two letters closest to a string and one letter immediately after a string using Notepad++?

How do you talk to someone whose loved one is dying?

Is honey really a supersaturated solution? Does heating to un-crystalize redissolve it or melt it?

Can a one-dimensional blade cut everything ? (chainsaw) (Sword)

Official degrees of earth’s rotation per day

Have researchers managed to "reverse time"? If so, what does that mean for physics?

Why did it take so long to abandon sail after steamships were demonstrated?

Describing a chess game in a novel

Is it possible to upcast ritual spells?

Do I need to be arrogant to get ahead?

What is the Japanese sound word for the clinking of money?

I am confused as to how the inverse of a certain function is found.

Meme-controlled people

Professor being mistaken for a grad student

A Cautionary Suggestion

Knife as defense against stray dogs

Recruiter wants very extensive technical details about all of my previous work

Is there a symmetric-key algorithm which we can use for creating a signature?

How do I hide Chekhov's Gun?

Instead of a Universal Basic Income program, why not implement a "Universal Basic Needs" program?

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

edited yesterday

I_Play_With_Data

1,212531

asked yesterday

william007william007

26919

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

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

  
  
  
  

add a comment | 

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

edited yesterday

I_Play_With_Data

1,212531

asked yesterday

william007william007

26919

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

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

  
  
  
  

add a comment | 

$begingroup$

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

edited yesterday

I_Play_With_Data

1,212531

asked yesterday

william007william007

26919

$endgroup$

share|improve this question

edited yesterday

I_Play_With_Data

1,212531

asked yesterday

william007william007

26919

share|improve this question

edited yesterday

I_Play_With_Data

1,212531

asked yesterday

william007william007

26919

edited yesterday

I_Play_With_Data

1,212531

edited yesterday

I_Play_With_Data

1,212531

asked yesterday

william007william007

26919

asked yesterday

william007william007

26919

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

answered yesterday

PreetPreet

3234

$endgroup$

add a comment | 

Your Answer

StackExchange.ifUsing("editor", function ()
return StackExchange.using("mathjaxEditing", function ()
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix)
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
);
);
, "mathjax-editing");

StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "557"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);

else
createEditor();

);

function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);



);

draft saved

draft discarded

Sign up or log in

StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);

Sign up using Google

Sign up using Facebook

Sign up using Email and Password

Post as a guest

Name

Email

Required, but never shown

StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f47334%2frevenue-projection%23new-answer', 'question_page');

);

Post as a guest

Name

Email

Required, but never shown

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

answered yesterday

PreetPreet

3234

$endgroup$

add a comment | 

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

answered yesterday

PreetPreet

3234

$endgroup$

add a comment | 

$begingroup$

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

answered yesterday

PreetPreet

3234

$endgroup$

share|improve this answer

answered yesterday

PreetPreet

3234

answered yesterday

PreetPreet

3234

answered yesterday

PreetPreet

3234