Body: <p>I can use any machine learning algorithms (but neural networks are better for me) to resolve this issue: use few elements as input (numerical) to predict more elements as output. In normal regression there are N elements with some features and can be used to target N elements with some features. In my mind this case is a small matrix to predict a bigger matrix: ideally the first and the last row of the table to target all the remainings.</p>
  <div class="s-table-container"><table class="s-table">
  <thead>
  <tr>
  <th>ID</th>
  <th>Feature 0</th>
  <th>Feature 1</th>
  <th>Feature 2</th>
  <th>...</th>
  <th>... Feature M</th>
  </tr>
  </thead>
  <tbody>
  <tr>
  <td>0</td>
  <td>23</td>
  <td>51</td>
  <td>147</td>
  <td>...</td>
  <td>2090</td>
  </tr>
  <tr>
  <td>1</td>
  <td>8000</td>
  <td>8200</td>
  <td>8500</td>
  <td>...</td>
  <td>15000</td>
  </tr>
  <tr>
  <td>2</td>
  <td>7500</td>
  <td>7700</td>
  <td>8100</td>
  <td>...</td>
  <td>17000</td>
  </tr>
  <tr>
  <td>3</td>
  <td>6500</td>
  <td>6800</td>
  <td>7000</td>
  <td>...</td>
  <td>15500</td>
  </tr>
  <tr>
  <td>4</td>
  <td>6600</td>
  <td>6900</td>
  <td>7100</td>
  <td>...</td>
  <td>14500</td>
  </tr>
  <tr>
  <td>...</td>
  <td>...</td>
  <td>...</td>
  <td>...</td>
  <td>...</td>
  <td>...</td>
  </tr>
  <tr>
  <td>Last</td>
  <td>23</td>
  <td>57</td>
  <td>172</td>
  <td>...</td>
  <td>2320</td>
  </tr>
  </tbody>
  </table></div>
  <p>(The ID column is only to explain the row used for the training and for the target dataset)</p>
  <p>I need to understand which algorithm can I use to use Row[0, last] as training to target Row[1:last-1].</p>
  <p>If it is possible, an ANN solution and TensorFlow framework is better for me.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-28T12:13:43.803Z
Comments.Text: The wording here is confusing, but that's ok because you are new to the subject and if you knew the right words already you would likely already be able to find the answer. But to clarify - does your data represent a *sequence* where each row follows from the previous one (or more)? And your goal is to predict next (or previous) row from one or more existing ones?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-28T13:37:30.867Z
Comments.Text: Thanks for the understanding! 
  It is not a sequence in the sense that row 1 follow the row 0, but it is more that with the first and the last row to predict what is in the middle. If it is not possible another option is to use the first row to predict an half matrix and the last row to predict the remaining matrix. So on the second question: the goal is predict more than 1 row
Comments.UserDisplayName: Cyr
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-28T16:30:26.110Z
Comments.Text: OK, that is still a sequence in the general sense - but you want to predict *all* the middle items?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-29T12:31:28.917Z
Comments.Text: yes, that's the idea! (Even if I may believe that the results won't be so accurate)
Comments.UserDisplayName: Cyr
Tags: <neural-networks><machine-learning><tensorflow><regression>
Title: Predict more elements than the input
UserDisplayName: Cyr
ThreadText: # Predict more elements than the input
  
  (Asked by: username_0 on 2024-08-27)
  
  I can use any machine learning algorithms (but neural networks are better for me) to resolve this issue: use few elements as input (numerical) to predict more elements as output. In normal regression there are N elements with some features and can be used to target N elements with some features. In my mind this case is a small matrix to predict a bigger matrix: ideally the first and the last row of the table to target all the remainings.
  
  | ID | Feature 0 | Feature 1 | Feature 2 | ... | ... Feature M |
  | --- | --- | --- | --- | --- | --- |
  | 0 | 23 | 51 | 147 | ... | 2090 |
  | 1 | 8000 | 8200 | 8500 | ... | 15000 |
  | 2 | 7500 | 7700 | 8100 | ... | 17000 |
  | 3 | 6500 | 6800 | 7000 | ... | 15500 |
  | 4 | 6600 | 6900 | 7100 | ... | 14500 |
  | ... | ... | ... | ... | ... | ... |
  | Last | 23 | 57 | 172 | ... | 2320 |
  
  (The ID column is only to explain the row used for the training and for the target dataset)
  
  I need to understand which algorithm can I use to use Row[0, last] as training to target Row[1:last-1].
  
  If it is possible, an ANN solution and TensorFlow framework is better for me.
  
  **Tags:** neural-networks, machine-learning, tensorflow, regression
  
  > **Comments:**
  > **username_1**: The wording here is confusing, but that's ok because you are new to the subject and if you knew the right words already you would likely already be able to find the answer. But to clarify - does your data represent a *sequence* where each row follows from the previous one (or more)? And your goal is to predict next (or previous) row from one or more existing ones?  
  > **username_0**: Thanks for the understanding!  It is not a sequence in the sense that row 1 follow the row 0, but it is more that with the first and the last row to predict what is in the middle. If it is not possible another option is to use the first row to predict an half matrix and the last row to predict the remaining matrix. So on the second question: the goal is predict more than 1 row  
  > **username_1**: OK, that is still a sequence in the general sense - but you want to predict *all* the middle items?  
  > **username_0**: yes, that's the idea! (Even if I may believe that the results won't be so accurate)  
  
  ---
  
  ## 0 answers
  
  