Answers.Body: <p>I think you can derive new features that should be more stable over time using PCA. A more simple approaches is to calculate the technical indicators of these numerical features such as RSI, moving average as they will represent the trend more effectively.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-02-26T03:12:12.163Z
Answers.LastActivityDate: 2024-02-26T03:12:12.163Z
Answers.UserDisplayName: Gene
Answers.Body: <blockquote>
  <p>The aim is to build a regressor capable of predicting next year sales.
  So, if I want to predict sales for 2024, I could use data from 2023,
  2022, 2021, and so on...</p>
  </blockquote>
  <p>it is not a Regressor in full-sence -- RegressionAnalysis &amp; <strong>TimeSeries Analysis</strong> have different aims &amp; different algorithms &amp; different conclusions... In regression analysis you investigate Dependency and you should analyse <a href="https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables" rel="nofollow noreferrer">Independent and identically distributed random variables</a>, but in TS present state is  (can be) dependent on previous state -- it is Markov Process, that can describe State-Space Models, aka TSAnalysis is -- and its aim is just to analyse <code>time(I.V.)</code> as feature and <code>response(D.V.)</code> depending on time, <strong>NOT</strong> any other features/causes for the certain response, like Inflation or etc. that you can imagine... all of them depending on time will have there own trend &amp; seasonal components, that can be used in the prediction... nothing about <em>regressing</em> -- do not mix two different kinds of analysis</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-06-01T18:00:38.037Z
Answers.LastActivityDate: 2024-06-01T18:00:38.037Z
Answers.UserDisplayName: JeeyCi
Body: <p>I have a financial dataset which has 10 years worth of data. The aim is to build a regressor capable of predicting next year sales. So, if I want to predict sales for 2024, I could use data from 2023, 2022, 2021, and so on...</p>
  <p>If I use all my features, I'm able to get a decent model, but with some feature selection/engineering, I believe we can have much better model, because some of my features are very noisy. So, I was trying to apply MDA (Mean Decrease accuracy) and analyze feature importance for a Random Forest, but the big thing is, the feature importance keeps varying a lot from year to year, so its really hard to select which features are noise and which are not.</p>
  <p>For instance, for 2023 the inflation is a very important feature, but back in 2018 it was not that important and was only adding noise. I thought about adding more features or trying to differentiate my features in order to have something more stable, but no success yet.</p>
  <p>So my question is, <strong>what are the best ways to tackle this?</strong></p>
  <p>Thanks for reading!</p>
  
Tags: <features><statistics><feature-engineering><random-forests>
Title: Non constant Feature Importance
UserDisplayName: Nick
ThreadText: # Non constant Feature Importance
  
  (Asked by: username_0 on 2024-02-25)
  
  I have a financial dataset which has 10 years worth of data. The aim is to build a regressor capable of predicting next year sales. So, if I want to predict sales for 2024, I could use data from 2023, 2022, 2021, and so on...
  
  If I use all my features, I'm able to get a decent model, but with some feature selection/engineering, I believe we can have much better model, because some of my features are very noisy. So, I was trying to apply MDA (Mean Decrease accuracy) and analyze feature importance for a Random Forest, but the big thing is, the feature importance keeps varying a lot from year to year, so its really hard to select which features are noise and which are not.
  
  For instance, for 2023 the inflation is a very important feature, but back in 2018 it was not that important and was only adding noise. I thought about adding more features or trying to differentiate my features in order to have something more stable, but no success yet.
  
  So my question is, **what are the best ways to tackle this?**
  
  Thanks for reading!
  
  **Tags:** features, statistics, feature-engineering, random-forests
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2024-02-26. Score: 1
  
  I think you can derive new features that should be more stable over time using PCA. A more simple approaches is to calculate the technical indicators of these numerical features such as RSI, moving average as they will represent the trend more effectively.
  
  
  ---
  
  ### Answer by username_2 on 2024-06-01. Score: 0
  
  > The aim is to build a regressor capable of predicting next year sales.
  > So, if I want to predict sales for 2024, I could use data from 2023,
  > 2022, 2021, and so on...
  
  it is not a Regressor in full-sence -- RegressionAnalysis & **TimeSeries Analysis** have different aims & different algorithms & different conclusions... In regression analysis you investigate Dependency and you should analyse [Independent and identically distributed random variables](https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables), but in TS present state is (can be) dependent on previous state -- it is Markov Process, that can describe State-Space Models, aka TSAnalysis is -- and its aim is just to analyse `time(I.V.)` as feature and `response(D.V.)` depending on time, **NOT** any other features/causes for the certain response, like Inflation or etc. that you can imagine... all of them depending on time will have there own trend & seasonal components, that can be used in the prediction... nothing about *regressing* -- do not mix two different kinds of analysis
  
  
  ---
  
  