Chronos: update chronos example in readme.md (#3154)
* update chronos example in readme.md * remove redundant import * update code
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					@ -155,25 +155,39 @@ To train a time series model with AutoML, first initialize [Orca Context](https:
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from bigdl.orca import init_orca_context
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					from bigdl.orca import init_orca_context
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#cluster_mode can be "local", "k8s" or "yarn"
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					#cluster_mode can be "local", "k8s" or "yarn"
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sc = init_orca_context(cluster_mode="yarn", cores=4, memory="10g", num_nodes=2, init_ray_on_spark=True)
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					init_orca_context(cluster_mode="yarn", cores=4, memory="10g", num_nodes=2, init_ray_on_spark=True)
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```
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					```
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Next, create an _AutoTSTrainer_.
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					Then, create _TSDataset_ for your data.
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					```python
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					from bigdl.chronos.data import TSDataset
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					tsdata_train, tsdata_valid, tsdata_test\
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					        = TSDataset.from_pandas(df, 
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					                                dt_col="dt_col", 
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					                                target_col="target_col", 
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					                                with_split=True, 
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					                                val_ratio=0.1, 
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					                                test_ratio=0.1)
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					```
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					Next, create an _AutoTSEstimator_.
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```python
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					```python
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from bigdl.chronos.autots.deprecated.forecast import AutoTSTrainer
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					from bigdl.chronos.autots import AutoTSEstimator
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trainer = AutoTSTrainer(dt_col="datetime", target_col="value")
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					autotsest = AutoTSEstimator(model='lstm')
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```
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					```
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Finally, call ```fit``` on _AutoTSTrainer_, which applies AutoML to find the best model and hyper-parameters; it returns a _TSPipeline_ which can be used for prediction or evaluation.
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					Finally, call ```fit``` on _AutoTSEstimator_, which applies AutoML to find the best model and hyper-parameters; it returns a _TSPipeline_ which can be used for prediction or evaluation.
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```python
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					```python
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#train a pipeline with AutoML support
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					#train a pipeline with AutoML support
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ts_pipeline = trainer.fit(train_df, validation_df)
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					ts_pipeline = autotsest.fit(data=tsdata_train,
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					                            validation_data=tsdata_valid)
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#predict
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					#predict
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ts_pipeline.predict(test_df)
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					ts_pipeline.predict(tsdata_test)
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```
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					```
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See the Chronos [user guide](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/chronos.html) and [example](https://bigdl.readthedocs.io/en/latest/doc/Chronos/QuickStart/chronos-autotsest-quickstart.html) for more details.
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					See the Chronos [user guide](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/chronos.html) and [example](https://bigdl.readthedocs.io/en/latest/doc/Chronos/QuickStart/chronos-autotsest-quickstart.html) for more details.
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