# Time Series - Sequence-to-Seqeuence Prediction **URL:** \ **Category:** DL4J\ **Created:** [June 21, 2021, 12:03pm UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459 "2021-06-21T12:03:20Z")\ **Posts on this page:** 11\ **Page:** 1
**Author:** ![clasch-student](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/clasch-student/32/563_2.png) [@clasch-student](https://community.konduit.ai/u/clasch-student)\ **Post date:** [June 21, 2021, 12:03pm UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459/1 "2021-06-21T12:03:20Z")
Hi there, Would like to predict one week of timeseries data (distance between time steps are always the same (data every two minutes)). So let’s say I have data from 4 weeks and would like to predict the 5th week. So it would be a “4-weeks-sequence-to-1-week-sequence” prediction. My data looks like that: train/features: Data of 4 weeks train/labels: Data of week 5th (week which follows after the train data) How would you handle such a use case? Would you prefer to work with an LSTM or an Auto-Encoder? ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [June 24, 2021, 7:06am UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459/2 "2021-06-24T07:06:04Z")
> [@clasch-student](#): > > es data (distance between time steps are always the same (data every two minutes)). So let’s say I have d @clasch-student sorry feel free to ping me if you don’t get a reply within a day or so. Sometimes replies slip through the cracks due to being busy. For time series, Conv1D or LSTM will be fine. I would just try both and see what works better for your use case. There’s no one size fits all solution. ---
**Author:** ![clasch-student](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/clasch-student/32/563_2.png) [@clasch-student](https://community.konduit.ai/u/clasch-student)\ **Post date:** [June 24, 2021, 11:11am UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459/3 "2021-06-24T11:11:00Z")
@agibsonccc No worries 🙂 Tried it out with LSTM and the results look quite good. What I was wondering is, if the format of my data is correct. I have a train\_features.csv with 168 time steps per line and a train\_labels.csv with 168 time steps per line. Line 1 of train\_features.csv is week 1 and line 1 of train\_labels.csv is week 2, line 2 of train\_features.csv is week 3 and line 2 of train\_labels.csv is week 4 and so on… What do you think about that? Is my data in a good shape or should I do this in another way to improve the learning/training process? train\_features.csv: ```auto 0 2 1 0 0 0 0 0 0 0 0 0 1 0 3 1 1 3 2 0 3 1 1 0 0 0 2 1 0 1 2 1 0 2 5 2 2 0 0 2 1 3 1 1 1 1 2 0 1 0 0 0 0 0 0 2 6 4 3 0 0 0 1 0 2 0 0 0 4 1 4 1 1 3 6 6 8 6 4 8 21 14 17 14 16 16 35 36 38 35 36 41 41 34 30 21 9 12 12 14 7 3 2 4 18 19 25 25 28 23 39 59 57 46 39 33 38 37 30 19 12 9 10 11 6 7 5 4 8 9 5 16 14 20 28 35 42 33 38 31 47 39 21 25 20 10 8 7 3 3 7 8 14 25 28 37 21 30 31 48 52 42 36 24 37 22 37 20 10 10 9 6 3 7 10 7 26 27 33 29 20 15 37 30 44 46 42 28 27 25 18 15 13 6 4 3 1 1 1 1 3 2 1 1 1 3 2 1 1 2 1 1 1 1 3 5 3 1 1 1 1 1 1 1 1 1 1 1 1 3 4 5 1 5 3 2 1 1 1 1 3 4 5 3 5 5 11 12 34 37 51 39 34 48 35 24 18 17 8 3 3 7 9 4 4 3 2 2 6 3 10 20 31 35 40 29 32 29 45 39 35 27 24 23 38 32 29 22 14 8 9 10 8 9 7 14 23 36 37 24 29 35 37 49 58 51 42 41 43 29 26 25 15 5 12 9 3 4 17 12 32 29 41 36 32 34 50 63 41 42 33 28 27 27 22 26 8 11 10 10 4 4 10 12 36 35 35 37 42 38 32 60 36 35 26 37 41 22 17 21 9 5 3 1 0 0 0 0 0 0 0 0 0 0 0 2 1 2 0 4 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 3 3 1 3 1 6 5 0 2 10 6 8 5 6 11 30 39 48 41 31 42 34 44 56 63 47 46 42 39 39 41 14 9 13 8 2 3 9 26 49 41 53 36 41 43 33 78 64 40 61 42 39 40 35 27 13 11 12 8 6 5 5 13 19 43 63 41 34 35 38 66 58 26 33 39 39 42 29 33 19 17 13 8 12 4 6 10 23 56 36 45 40 30 39 41 48 48 37 45 40 42 47 19 8 3 5 3 0 2 3 2 18 33 30 30 23 32 35 32 52 56 45 35 29 34 22 15 10 4 1 0 0 0 0 0 0 0 0 0 0 0 2 1 2 4 3 4 2 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 3 0 2 2 5 6 2 1 1 7 5 1 5 2 3 4 1 0 8 29 45 52 42 42 32 49 48 58 53 39 44 40 21 28 20 9 6 4 2 4 3 9 6 45 40 57 45 37 28 42 59 65 53 45 36 38 41 50 22 19 10 3 7 4 1 6 13 34 37 46 53 42 34 38 45 63 67 48 39 36 32 43 36 21 6 7 12 10 2 5 20 23 36 33 37 36 32 44 58 60 66 52 44 30 39 25 21 13 12 4 10 8 3 11 7 18 16 25 21 19 20 35 28 35 35 22 16 32 26 22 19 11 1 0 1 2 1 0 0 0 0 0 2 1 0 2 2 3 1 2 3 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 3 6 6 1 3 5 4 8 8 2 14 14 33 30 33 36 32 29 41 43 49 55 44 33 26 27 35 21 19 11 15 8 4 2 17 9 41 50 33 45 25 22 38 46 54 56 34 32 23 23 30 32 14 10 9 12 5 4 3 18 27 42 41 27 28 37 62 52 65 46 42 35 32 37 33 38 20 18 9 8 7 11 19 14 38 45 48 33 28 32 44 46 60 44 33 42 47 53 32 17 12 5 14 4 4 4 9 6 31 29 39 19 21 10 19 47 37 21 27 30 27 11 26 14 8 5 1 5 2 1 0 0 1 0 0 0 0 0 2 0 2 2 1 5 4 6 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 3 0 0 0 1 0 0 0 2 10 7 6 8 22 31 49 52 55 27 35 39 40 60 56 57 37 25 24 31 21 15 16 11 14 7 8 12 23 26 37 53 45 20 32 64 49 43 64 40 30 38 34 29 25 16 13 14 9 4 5 12 32 30 33 36 40 33 47 52 59 56 50 41 51 53 33 27 15 17 12 8 5 6 7 10 12 28 38 43 31 44 37 57 64 54 49 43 48 32 40 19 10 9 9 10 5 9 5 2 15 21 23 26 29 25 33 48 31 27 31 13 4 28 31 14 13 9 1 0 0 0 0 0 0 0 0 0 0 2 3 3 0 0 3 0 2 1 1 0 1 0 0 0 0 3 0 2 0 0 0 0 0 0 0 1 4 4 1 1 3 8 4 4 2 1 1 5 6 9 6 2 20 35 29 49 40 33 16 22 30 34 44 38 40 31 40 33 23 25 21 10 8 8 5 4 23 30 32 39 35 26 34 43 41 48 46 46 31 26 39 35 23 27 16 5 6 9 8 18 34 34 36 31 29 32 41 36 54 49 47 42 45 40 32 15 11 9 12 7 6 3 3 10 17 23 25 31 24 18 14 23 34 40 36 29 36 35 28 23 28 13 3 11 5 0 5 8 9 15 27 31 23 16 18 30 43 40 41 39 34 36 38 23 19 11 10 0 2 0 0 2 0 0 0 2 1 2 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 1 0 0 0 1 1 0 0 4 8 2 0 0 0 0 0 8 5 3 10 15 26 32 29 22 21 30 34 46 30 35 36 24 33 23 27 27 22 10 8 7 6 8 10 15 32 37 40 32 29 28 24 41 24 45 34 28 28 26 32 19 20 14 10 9 7 6 6 20 28 32 40 23 26 19 36 40 31 36 36 35 45 37 44 21 17 13 13 14 11 8 14 24 32 26 47 38 33 26 40 39 46 35 28 51 44 24 23 23 ``` train\_labels.csv: ```auto 13 11 9 7 8 1 8 13 35 36 31 31 18 30 27 30 34 43 32 33 34 23 17 18 17 13 5 4 1 1 1 1 2 1 1 1 1 1 1 2 3 2 2 6 6 8 5 4 2 1 1 1 3 2 2 3 1 1 1 1 1 1 1 1 1 1 2 1 5 3 4 4 2 6 2 4 5 2 10 15 23 35 31 40 29 30 43 45 49 44 40 35 33 37 42 40 24 19 20 12 6 6 18 14 39 32 35 39 35 32 36 64 54 51 45 36 33 36 28 36 18 10 12 2 3 4 4 17 8 20 29 41 45 48 70 46 45 69 58 52 39 27 23 27 21 12 12 10 4 6 7 16 28 33 40 33 28 21 40 41 42 54 34 36 31 26 14 21 10 7 7 10 4 0 8 11 25 22 31 38 28 37 36 37 37 38 35 34 35 29 20 14 3 3 3 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 2 3 1 1 0 0 0 3 0 0 1 0 0 0 0 0 1 0 0 0 0 0 2 0 2 1 1 1 0 4 2 3 0 10 15 22 34 50 42 33 42 44 44 47 43 38 42 43 43 30 20 15 9 12 24 12 6 15 12 23 48 34 27 22 33 40 43 55 41 42 28 46 46 22 18 8 4 10 10 8 6 11 16 20 26 22 25 20 24 35 49 84 60 46 30 33 37 26 22 8 7 6 8 2 2 3 16 27 53 39 21 26 30 34 47 45 50 43 34 42 41 27 28 17 13 12 7 2 2 2 15 23 40 52 39 38 28 38 31 36 31 36 26 30 34 24 13 6 7 4 1 0 2 2 1 1 0 0 0 0 4 2 0 2 1 0 0 0 2 1 4 2 1 2 6 4 2 2 1 5 3 1 1 0 0 0 0 4 0 0 5 6 7 1 0 1 0 4 5 6 1 6 9 49 46 44 33 30 34 58 53 60 56 64 48 42 33 22 25 13 14 8 7 2 7 7 15 34 31 33 44 35 24 46 50 66 47 51 39 39 29 36 22 33 11 8 8 8 5 3 4 18 35 43 43 32 39 38 43 48 46 39 38 46 51 25 28 23 15 8 6 5 1 2 6 20 32 44 35 26 22 49 47 58 48 41 31 21 38 31 25 15 5 7 9 12 3 11 19 20 36 57 36 23 18 33 27 31 32 28 28 28 21 7 5 4 5 7 3 4 8 3 0 1 2 2 0 0 0 0 0 1 4 1 3 3 2 0 0 0 0 0 2 4 1 0 6 0 0 2 1 0 0 0 1 2 1 0 0 0 0 0 0 0 0 4 7 5 2 17 11 36 55 37 32 29 39 42 39 47 50 39 46 47 44 33 18 9 7 8 8 4 3 12 15 34 28 40 44 29 41 46 63 59 44 44 43 41 35 25 28 21 6 4 4 5 0 9 4 27 48 38 29 19 49 48 59 63 45 39 35 38 34 50 29 28 10 8 7 4 0 7 10 27 31 31 29 27 31 44 45 51 45 42 46 28 21 26 20 9 11 7 5 2 6 7 16 36 39 43 35 29 25 32 45 45 32 40 26 29 21 12 11 3 5 2 0 1 0 0 1 0 0 4 1 3 1 0 2 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 1 1 0 6 2 3 0 2 1 0 2 0 0 0 3 6 2 9 12 27 38 46 42 34 49 48 52 49 57 53 39 33 52 44 24 10 6 4 3 2 0 9 10 37 32 34 37 27 30 38 57 45 43 48 45 45 38 32 31 13 9 7 7 2 2 7 12 35 29 46 34 34 39 47 64 46 55 42 29 37 33 39 27 26 19 18 11 7 6 12 17 38 33 30 43 33 37 35 62 64 51 48 58 42 29 30 20 7 6 14 6 3 5 12 10 13 29 35 35 32 26 27 31 42 24 36 28 25 10 9 8 3 1 0 1 0 0 0 0 0 1 0 0 0 2 0 0 2 5 0 0 2 0 0 0 0 0 4 2 1 1 0 0 0 0 4 3 0 0 0 2 1 1 1 0 3 1 0 0 1 0 6 7 5 4 11 13 31 45 47 55 42 50 40 55 54 55 35 34 26 30 18 13 8 11 7 9 8 8 11 13 29 34 36 31 27 27 53 47 68 35 43 32 49 33 26 14 13 10 11 9 6 1 11 31 34 49 46 43 22 43 64 55 40 53 46 34 23 16 8 13 9 2 3 1 5 5 15 14 36 44 39 21 22 27 48 54 47 36 26 25 28 20 22 11 16 14 9 8 8 2 1 2 4 5 4 3 2 5 9 18 25 32 30 31 23 32 35 19 8 2 7 2 0 0 2 0 1 0 2 0 0 0 0 0 0 2 2 3 1 0 1 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 2 2 1 2 0 0 0 0 0 1 2 1 2 1 2 0 3 0 2 5 2 4 3 16 20 24 27 38 28 27 26 30 34 18 10 5 12 8 4 10 10 35 32 35 30 20 26 27 46 43 49 36 44 40 30 27 17 7 13 8 8 6 7 6 8 27 24 35 29 28 30 30 31 43 28 45 43 35 15 27 22 20 20 7 5 3 2 2 3 18 28 33 28 22 38 33 48 47 50 54 34 23 32 34 27 14 11 3 5 2 5 7 13 29 34 22 32 27 34 25 39 37 34 44 43 24 18 17 9 11 8 2 2 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 2 3 1 0 0 2 1 2 1 4 8 15 32 41 22 33 40 39 54 65 59 49 44 45 47 46 25 20 21 15 7 13 8 4 7 10 21 32 38 38 49 42 41 52 42 46 31 28 25 36 22 16 9 13 5 2 2 5 7 17 34 30 34 35 33 36 26 38 41 32 31 41 41 32 32 26 18 17 6 5 7 4 7 6 29 27 54 45 32 35 46 46 54 38 41 34 43 33 28 17 ``` ---
**Author:** ![clasch-student](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/clasch-student/32/563_2.png) [@clasch-student](https://community.konduit.ai/u/clasch-student)\ **Post date:** [June 28, 2021, 6:31am UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459/4 "2021-06-28T06:31:05Z")
@agibsonccc Please advise 🙂 ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [June 29, 2021, 9:18am UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459/5 "2021-06-29T09:18:51Z")
@clasch-student yes this all looks good. See here for the description: [https://deeplearning4j.konduit.ai/models/recurrent](https://deeplearning4j.konduit.ai/models/recurrent) As long as you follow the format you’re fine. Just note for supervised learning if you are doing regression, you may want to consider label normalization in addition to input normalization. ---
**Author:** ![clasch-student](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/clasch-student/32/563_2.png) [@clasch-student](https://community.konduit.ai/u/clasch-student)\ **Post date:** [June 29, 2021, 1:51pm UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459/6 "2021-06-29T13:51:40Z")
> [@agibsonccc](#): > > [https://deeplearning4j.konduit.ai/models/recurrent](https://deeplearning4j.konduit.ai/models/recurrent) Hi @agibsonccc Great - thanks for your help - really appreciate it! Another question popped up: I am facing with “gaps” in the time series data. Is there any possiblity to fill them automatically in DL4J or do I need to fill them with e.g. Interpolation on Data Preparation part (before Training)? ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [June 30, 2021, 1:31am UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459/7 "2021-06-30T01:31:25Z")
@clasch-student Generally interopolation or approximation would be the way to \go. At this point it will depend on the characteristics of your time series data though. Seasonality being one example. Could you elaborate on the gaps a little bit? ---
**Author:** ![clasch-student](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/clasch-student/32/563_2.png) [@clasch-student](https://community.konduit.ai/u/clasch-student)\ **Post date:** [July 5, 2021, 10:04am UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459/8 "2021-07-05T10:04:18Z")
Hi @agibsonccc Decided to fill the gaps with interpolation, but did it “manually” - is there a possibility to do it with DL4J? Maybe something similar like [pandas.DataFrame.interpolate — pandas 1.4.3 documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.interpolate.html) ? ---
**Author:** ![clasch-student](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/clasch-student/32/563_2.png) [@clasch-student](https://community.konduit.ai/u/clasch-student)\ **Post date:** [July 20, 2021, 7:43am UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459/9 "2021-07-20T07:43:03Z")
@agibsonccc Please advise 🙂 ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [July 20, 2021, 2:14pm UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459/10 "2021-07-20T14:14:52Z")
@clasch-student you might want to give tablesaw a shot: [tablesaw/InterpolatorTest.java at master · jtablesaw/tablesaw · GitHub](https://github.com/jtablesaw/tablesaw/blob/master/core/src/test/java/tech/tablesaw/interpolation/InterpolatorTest.java) then just convert the results to ndarrays. You could also look at using our python execution. You can pass ndarrays directly in memory to be parsed as numpy arrays to python scripts and get the results back out in java. ---
**Author:** ![clasch-student](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/clasch-student/32/563_2.png) [@clasch-student](https://community.konduit.ai/u/clasch-student)\ **Post date:** [July 22, 2021, 8:24pm UTC](https://community.konduit.ai/t/time-series-sequence-to-seqeuence-prediction/1459/11 "2021-07-22T20:24:24Z")
Great - will have a look at it - thanks 🙂