[timeseries] add some basic block and deepAR model #2027
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Codecov ReportBase: 72.08% // Head: 71.36% // Decreases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## master #2027 +/- ##
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- Coverage 72.08% 71.36% -0.73%
- Complexity 5126 6275 +1149
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Files 473 624 +151
Lines 21970 27795 +5825
Branches 2351 2997 +646
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+ Hits 15838 19835 +3997
- Misses 4925 6500 +1575
- Partials 1207 1460 +253
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| * and limitations under the License. | ||
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| package ai.djl.timeseries.block; |
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) * feature: add TimeSeriesDataset and training transform * feature: some basic block and deepar model * feature: add train example * feature: add m5-demo and air passengers demo Co-authored-by: Carkham <1302112560@qq.com> Co-authored-by: Frank Liu <frankfliu2000@gmail.com> Co-authored-by: KexinFeng <fengx463@umn.edu>
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Description
This PR adds two new sections for creating trainable models.
The first part is embedding and scaler, which are responsible for encoding the input features and scaling the target values.
The second part is that we implemented a deepAR network model with the above-mentioned blcok, which refers to the implementation in gluonTS.
Since the model predicts a probability distribution, we need to override the different forward methods when training and inference with different block.