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guillaume-chevalier
seq2seq-signal-prediction
Jupyter Notebook2017–2020
guillaume-chevalier / seq2seq-signal-prediction

seq2seq-signal-prediction

Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier

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2Distinct people
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1.1KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month3 dated mentions

Observed mentions: 2017-06-20 04:09:02 UTC to 2020-01-20 02:56:20 UTC.

012017-06-01 00:00:00 UTC to 2017-07-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJun 20172017-07-01 00:00:00 UTC to 2017-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-08-01 00:00:00 UTC to 2017-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-09-01 00:00:00 UTC to 2017-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-10-01 00:00:00 UTC to 2017-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-11-01 00:00:00 UTC to 2017-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-12-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-01-01 00:00:00 UTC to 2018-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-02-01 00:00:00 UTC to 2018-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-03-01 00:00:00 UTC to 2018-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-04-01 00:00:00 UTC to 2018-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-05-01 00:00:00 UTC to 2018-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-06-01 00:00:00 UTC to 2018-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-07-01 00:00:00 UTC to 2018-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-08-01 00:00:00 UTC to 2018-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-09-01 00:00:00 UTC to 2018-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsSep 20182018-10-01 00:00:00 UTC to 2018-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-11-01 00:00:00 UTC to 2018-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-12-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2019-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-02-01 00:00:00 UTC to 2019-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-03-01 00:00:00 UTC to 2019-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-04-01 00:00:00 UTC to 2019-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-05-01 00:00:00 UTC to 2019-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-06-01 00:00:00 UTC to 2019-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-07-01 00:00:00 UTC to 2019-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-08-01 00:00:00 UTC to 2019-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-09-01 00:00:00 UTC to 2019-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-10-01 00:00:00 UTC to 2019-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-11-01 00:00:00 UTC to 2019-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-12-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2020-02-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJan 2020

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jun 20172017-06-01 00:00:00 UTC2017-07-01 00:00:00 UTC110
Jul 20172017-07-01 00:00:00 UTC2017-08-01 00:00:00 UTC000
Aug 20172017-08-01 00:00:00 UTC2017-09-01 00:00:00 UTC000
Sep 20172017-09-01 00:00:00 UTC2017-10-01 00:00:00 UTC000
Oct 20172017-10-01 00:00:00 UTC2017-11-01 00:00:00 UTC000
Nov 20172017-11-01 00:00:00 UTC2017-12-01 00:00:00 UTC000
Dec 20172017-12-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
Jan 20182018-01-01 00:00:00 UTC2018-02-01 00:00:00 UTC000
Feb 20182018-02-01 00:00:00 UTC2018-03-01 00:00:00 UTC000
Mar 20182018-03-01 00:00:00 UTC2018-04-01 00:00:00 UTC000
Apr 20182018-04-01 00:00:00 UTC2018-05-01 00:00:00 UTC000
May 20182018-05-01 00:00:00 UTC2018-06-01 00:00:00 UTC000
Jun 20182018-06-01 00:00:00 UTC2018-07-01 00:00:00 UTC000
Jul 20182018-07-01 00:00:00 UTC2018-08-01 00:00:00 UTC000
Aug 20182018-08-01 00:00:00 UTC2018-09-01 00:00:00 UTC000
Sep 20182018-09-01 00:00:00 UTC2018-10-01 00:00:00 UTC101
Oct 20182018-10-01 00:00:00 UTC2018-11-01 00:00:00 UTC000
Nov 20182018-11-01 00:00:00 UTC2018-12-01 00:00:00 UTC000
Dec 20182018-12-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
Jan 20192019-01-01 00:00:00 UTC2019-02-01 00:00:00 UTC000
Feb 20192019-02-01 00:00:00 UTC2019-03-01 00:00:00 UTC000
Mar 20192019-03-01 00:00:00 UTC2019-04-01 00:00:00 UTC000
Apr 20192019-04-01 00:00:00 UTC2019-05-01 00:00:00 UTC000
May 20192019-05-01 00:00:00 UTC2019-06-01 00:00:00 UTC000
Jun 20192019-06-01 00:00:00 UTC2019-07-01 00:00:00 UTC000
Jul 20192019-07-01 00:00:00 UTC2019-08-01 00:00:00 UTC000
Aug 20192019-08-01 00:00:00 UTC2019-09-01 00:00:00 UTC000
Sep 20192019-09-01 00:00:00 UTC2019-10-01 00:00:00 UTC000
Oct 20192019-10-01 00:00:00 UTC2019-11-01 00:00:00 UTC000
Nov 20192019-11-01 00:00:00 UTC2019-12-01 00:00:00 UTC000
Dec 20192019-12-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
Jan 20202020-01-01 00:00:00 UTC2020-02-01 00:00:00 UTC110

What Hacker News says

3 sources
GChevalier·Sep 2, 2018
I see here that original poster (OP) of the post tried to use many-to-one LSTMs instead of many-to-many LSTMs. I tell that first by looking at the charts. Then I saw the method named "predict_point_by_point" with the comment "Predict each timestep given the last sequence of true data, in effect only predicting 1 step ahead each time" in his code here: https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Se... I strongly think the system would be better to perform many predictions at once instead, using seq2seq neural networks. The problem is properly explained here at the beginning of this other post: https://github.com/LukeTonin/keras-seq-2-seq-signal-predicti... This other post is, in turn, derived from my original project here doing seq2seq predictions with TensorFlow: https://github.com/guillaume-chevalier/seq2seq-signal-predic..
Read the rest
. OP also forgot to cite the image I made: https://en.wikipedia.org/wiki/Long_short-term_memory#/media/... Well, glad to see that some similar work as mine can get this much traction on HN. I would have loved to get this much traction when I did my post, too. Anyway, I would suggest OP to take a look at seq2seq, as it objectively performs better (and without the "laggy drift" visual effect observed as in OP's figure named "S&P500 multi-sequence prediction"). In other words, using many-to-one neural architectures creates some kind of feedback which doesn't happen with seq2seq which doesn't build on its own accumulated error. It has a decoder with different weights than the encoder, and can be deep (stacked).
In Time Series Prediction Using LSTM Deep Neural Networks

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