Answers.Body: <p>Many papers have been published on CNN, LSTM, and CNN-LSTM for time series. From the literature and my experience, I conclude that CNN-LSTM outperforms CNN and LSTM models. Here are two relevant papers on stock price time series forecasting:</p>
  <p><a href="https://www.hindawi.com/journals/complexity/2020/6622927/" rel="nofollow noreferrer">Wenjie Lu, Jiazheng Li, Yifan Li, Aijun Sun, &amp; Jingyang Wang. (2020). A CNN-LSTM-Based Model to Forecast Stock Prices. Complexity, 2020. https://doi.org/10.1155/2020/6622927</a></p>
  <p>The authors' conclusions:</p>
  <blockquote>
  <p>This paper takes the relevant data of the Shanghai Composite Index as an example to verify the experimental results. The experimental results show that the CNN-LSTM has the highest forecasting accuracy and the best performance compared with the MLP, CNN, RNN, LSTM, and CNN-RNN.</p>
  </blockquote>
  <p><a href="https://www.hindawi.com/journals/sp/2021/2446543/" rel="nofollow noreferrer">Chen, Y., Fang, R., Liang, T., Sha, Z., Li, S., Yi, Y., Zhou, W., &amp; Song, H. (2021). Stock Price Forecast Based on CNN-BiLSTM-ECA Model. Scientific Programming, 1–20. https://doi.org/10.1155/2021/2446543</a></p>
  <p>The authors' conclusions:</p>
  <blockquote>
  <p>The proposed model is compared with CNN, LSTM, BiLSTM, CNNLSTM, CNN-BiLSTM, BiLSTM-ECA, and CNN-LSTMECA network models on three datasets. The experimental results show that the proposed model has the highest prediction accuracy and the best performance.</p>
  </blockquote>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-24T17:19:28.217Z
Answers.LastActivityDate: 2022-07-24T17:33:11.823Z
Answers.LastEditDate: 2022-07-24T17:33:11.823Z
Answers.UserDisplayName: Brian O'Donnell
Answers.Body: <p>In my opinion, if your dataset has correlation with its neighbors and there is some kind of sequence involved in it, then CNN-LSTM would provide you with much better results than only CNN or LSTM.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-24T09:44:31.493Z
Answers.Text: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).
Answers.UserDisplayName: Community
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-24T07:52:37.397Z
Answers.LastActivityDate: 2022-07-24T07:52:37.397Z
Answers.UserDisplayName: Nakul Mehta
Answers.Body: <p>CNN can be used as good feature extractor to help LSTM layers train efficiently , normally when your input contains huge number of features (images , audio) and you need to project these features into learnable lower dimension space use CNN before LSTM</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-24T20:40:43.897Z
Answers.LastActivityDate: 2022-07-24T20:40:43.897Z
Answers.UserDisplayName: Mohamed MosȜd
Body: <p>I am working on LSTM and CNN to solve the time series prediction problem.</p>
  <p>I have seen some tutorial examples of time series prediction using CNN-LSTM. But I don't know if it is better than what I predicted using LSTM.</p>
  <p>Could using LSTM and CNN together be better than predicting using LSTM alone?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-03T01:08:45.567Z
Comments.Text: Have a look at https://stats.stackexchange.com/questions/10162/how-to-apply-neural-network-to-time-series-forecasting/10196#10196
Comments.UserDisplayName: kjetil b halvorsen
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-08T11:24:59.580Z
Comments.Text: You wrote "I have seen some tutorial examples of time series prediction using CNN-LSTM". Can you please link us to those tutorials? Moreover, you should probably describe your dataset (i.e. is it a sequence of images? or how many data points do you have?).
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-24T17:02:51.130Z
Comments.Text: Similar to https://stats.stackexchange.com/questions/578508/cnn-lstm-or-lstm-better-for-univariate-time-series-forecasting although no answers posted.
Comments.UserDisplayName: Brian O'Donnell
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-24T17:24:53.127Z
Comments.Text: Please provide details on the time series. For example, is it rainfall in the Amazon, prices of airfares, or is it something chaotic like stock prices? In addition, is your data univariate or multivariate?
Comments.UserDisplayName: Brian O'Donnell
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-08-24T10:26:10.260Z
Comments.Text: It could, but it doesn't mean it always will. I guess that's why it can be called  "Data Science" problem, because every dataset is different and needs its own little "research". So what I mean is that you have to implement CNN-LSTM model and see for yourself.
Comments.UserDisplayName: GKozinski
Tags: <deep-learning><convolutional-neural-networks><long-short-term-memory><time-series>
Title: Time series prediction using LSTM and CNN-LSTM: which is better?
UserDisplayName: KYH
ThreadText: # Time series prediction using LSTM and CNN-LSTM: which is better?
  
  (Asked by: username_0 on 2020-12-08)
  
  I am working on LSTM and CNN to solve the time series prediction problem.
  
  I have seen some tutorial examples of time series prediction using CNN-LSTM. But I don't know if it is better than what I predicted using LSTM.
  
  Could using LSTM and CNN together be better than predicting using LSTM alone?
  
  **Tags:** deep-learning, convolutional-neural-networks, long-short-term-memory, time-series
  > **Comments:**
  > **username_2**: You wrote "I have seen some tutorial examples of time series prediction using CNN-LSTM". Can you please link us to those tutorials? Moreover, you should probably describe your dataset (i.e. is it a sequence of images? or how many data points do you have?).  
  > **username_1**: Have a look at https://stats.stackexchange.com/questions/10162/how-to-apply-neural-network-to-time-series-forecasting/10196#10196  
  > **username_3**: Similar to https://stats.stackexchange.com/questions/578508/cnn-lstm-or-lstm-better-for-univariate-time-series-forecasting although no answers posted.  
  > **username_3**: Please provide details on the time series. For example, is it rainfall in the Amazon, prices of airfares, or is it something chaotic like stock prices? In addition, is your data univariate or multivariate?  
  > **username_4**: It could, but it doesn't mean it always will. I guess that's why it can be called  "Data Science" problem, because every dataset is different and needs its own little "research". So what I mean is that you have to implement CNN-LSTM model and see for yourself.  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_3 on 2022-07-24. Score: 1 | Accepted answer
  
  Many papers have been published on CNN, LSTM, and CNN-LSTM for time series. From the literature and my experience, I conclude that CNN-LSTM outperforms CNN and LSTM models. Here are two relevant papers on stock price time series forecasting:
  
  [Wenjie Lu, Jiazheng Li, Yifan Li, Aijun Sun, & Jingyang Wang. (2020). A CNN-LSTM-Based Model to Forecast Stock Prices. Complexity, 2020. https://doi.org/10.1155/2020/6622927](https://www.hindawi.com/journals/complexity/2020/6622927/)
  
  The authors' conclusions:
  
  > This paper takes the relevant data of the Shanghai Composite Index as an example to verify the experimental results. The experimental results show that the CNN-LSTM has the highest forecasting accuracy and the best performance compared with the MLP, CNN, RNN, LSTM, and CNN-RNN.
  
  [Chen, Y., Fang, R., Liang, T., Sha, Z., Li, S., Yi, Y., Zhou, W., & Song, H. (2021). Stock Price Forecast Based on CNN-BiLSTM-ECA Model. Scientific Programming, 1–20. https://doi.org/10.1155/2021/2446543](https://www.hindawi.com/journals/sp/2021/2446543/)
  
  The authors' conclusions:
  
  > The proposed model is compared with CNN, LSTM, BiLSTM, CNNLSTM, CNN-BiLSTM, BiLSTM-ECA, and CNN-LSTMECA network models on three datasets. The experimental results show that the proposed model has the highest prediction accuracy and the best performance.
  
  
  ---
  
  ### Answer by username_5 on 2022-07-24. Score: 0
  
  In my opinion, if your dataset has correlation with its neighbors and there is some kind of sequence involved in it, then CNN-LSTM would provide you with much better results than only CNN or LSTM.
  
  > **Comments:**
  > **username_6**: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  
  
  ---
  
  ### Answer by username_7 on 2022-07-24. Score: 0
  
  CNN can be used as good feature extractor to help LSTM layers train efficiently , normally when your input contains huge number of features (images , audio) and you need to project these features into learnable lower dimension space use CNN before LSTM
  
  
  ---
  
  