@@ -206,15 +206,22 @@ IoTDB> show models
206206+ -- -------------------+--------------------+--------------+---------+
207207| ModelId| ModelType| Category| State|
208208+ -- -------------------+--------------------+--------------+---------+
209- | arima| arima| BUILT- IN | ACTIVE|
209+ | arima| Arima| BUILT- IN | ACTIVE|
210+ | holtwinters| HoltWinters| BUILT- IN | ACTIVE|
211+ |exponential_smoothing|ExponentialSmoothing| BUILT- IN | ACTIVE|
212+ | naive_forecaster| NaiveForecaster| BUILT- IN | ACTIVE|
213+ | stl_forecaster| StlForecaster| BUILT- IN | ACTIVE|
214+ | gaussian_hmm| GaussianHmm| BUILT- IN | ACTIVE|
215+ | gmm_hmm| GmmHmm| BUILT- IN | ACTIVE|
216+ | stray| Stray| BUILT- IN | ACTIVE|
210217| custom| | USER- DEFINED| ACTIVE|
211- | timerxl| timer - xl | BUILT- IN | LOADING|
212- | sundial| timer - sundial | BUILT- IN | ACTIVE|
213- | sundialx_1| timer - sundial | FINE- TUNED| ACTIVE|
214- | sundialx_2| timer - sundial | FINE- TUNED| ACTIVE|
215- | sundialx| timer - sundial | FINE- TUNED| ACTIVE|
216- | sundialx_4| timer - sundial | FINE- TUNED| TRAINING|
217- | sundialx_5| timer - sundial | FINE- TUNED| FAILED|
218+ | timerxl| Timer - XL | BUILT- IN | LOADING|
219+ | sundial| Timer - Sundial | BUILT- IN | ACTIVE|
220+ | sundialx_1| Timer - Sundial | FINE- TUNED| ACTIVE|
221+ | sundialx_2| Timer - Sundial | FINE- TUNED| ACTIVE|
222+ | sundialx| Timer - Sundial | FINE- TUNED| ACTIVE|
223+ | sundialx_4| Timer - Sundial | FINE- TUNED| TRAINING|
224+ | sundialx_5| Timer - Sundial | FINE- TUNED| FAILED|
218225+ -- -------------------+--------------------+--------------+---------+
219226```
220227
@@ -503,7 +510,7 @@ On this dataset, the model inference function of IoTDB-ML can predict the oil te
503510Users can import the ETT dataset into IoTDB using ` import-data.sh` in the tools folder
504511
505512` ` Bash
506- bash ./import-data.sh -ft csv -h 127.0.0.1 -p 6667 -u root -pw root -s /path/ETTh1.csv
513+ bash ./import-data.sh -ft csv -h 127.0.0.1 -p 6667 -u root -pw root -s /path/ETTh1.csv
507514` `
508515
509516# ### Step 2: Model Import
@@ -572,8 +579,8 @@ On this dataset, the model inference function of IoTDB-ML can predict the C-phas
572579
573580Users can import the dataset using ` import-data.sh` in the tools folder
574581
575- ` ` ` Bash
576- bash ./import-data.sh -ft csv -h 127.0.0.1 -p 6667 -u root -pw root -s /path/data.csv
582+ ` ` ` Bash
583+ bash ./import-data.sh -ft csv -h 127.0.0.1 -p 6667 -u root -pw root -s /path/data.csv
577584` ` `
578585
579586# ### Step 2: Model Import
@@ -635,8 +642,8 @@ On this dataset, the model inference function of IoTDB-ML can empower the transp
635642
636643Users can import the dataset using ` import-data.sh` in the tools folder
637644
638- ` ` Bash
639- bash ./import-data.sh -ft csv -h 127.0.0.1 -p 6667 -u root -pw root -s /path/data.csv
645+ ` ` Bash
646+ bash ./import-data.sh -ft csv -h 127.0.0.1 -p 6667 -u root -pw root -s /path/data.csv
640647` `
641648
642649# ### Step 2: Model Inference
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