Chronos: Sync file paths in doc, branch-2.0-->main (#4091)
* change branch-2.0 to main * change branch-2.0 to main * colab and github
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Anomaly Detection detects abnormal samples in a given time series. _Chronos_ provides a set of unsupervised anomaly detectors.
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View some examples notebooks for [Datacenter AIOps](https://github.com/intel-analytics/BigDL/tree/branch-2.0/python/chronos/use-case/AIOps).
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View some examples notebooks for [Datacenter AIOps][AIOps].
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## **1. ThresholdDetector**
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ThresholdDetector detects anomaly based on threshold. It can be used to detect anomaly on a given time series ([notebook](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb)), or used together with [Forecasters](#forecasting) to detect anomaly on new coming samples ([notebook](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb)).
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ThresholdDetector detects anomaly based on threshold. It can be used to detect anomaly on a given time series ([notebook][AIOps_anomaly_detect_unsupervised]), or used together with [Forecasters](#forecasting) to detect anomaly on new coming samples ([notebook][AIOps_anomaly_detect_unsupervised_forecast_based]).
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View [ThresholdDetector API Doc](../../PythonAPI/Chronos/anomaly_detectors.html#chronos-model-anomaly-th-detector) for more details.
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@ -15,10 +15,15 @@ View [ThresholdDetector API Doc](../../PythonAPI/Chronos/anomaly_detectors.html#
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AEDetector detects anomaly based on the reconstruction error of an autoencoder network.
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View anomaly detection [notebook](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb) and [AEDetector API Doc](../../PythonAPI/Chronos/anomaly_detectors.html#chronos-model-anomaly-ae-detector) for more details.
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View anomaly detection [notebook][AIOps_anomaly_detect_unsupervised] and [AEDetector API Doc](../../PythonAPI/Chronos/anomaly_detectors.html#chronos-model-anomaly-ae-detector) for more details.
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## **3. DBScanDetector**
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DBScanDetector uses DBSCAN clustering algortihm for anomaly detection.
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View anomaly detection [notebook](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb) and [DBScanDetector API Doc](../../PythonAPI/Chronos/anomaly_detectors.html#chronos-model-anomaly-dbscan-detector) for more details.
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View anomaly detection [notebook][AIOps_anomaly_detect_unsupervised] and [DBScanDetector API Doc](../../PythonAPI/Chronos/anomaly_detectors.html#chronos-model-anomaly-dbscan-detector) for more details.
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[AIOps]:<https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/AIOps>
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[AIOps_anomaly_detect_unsupervised]:<https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb>
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[AIOps_anomaly_detect_unsupervised_forecast_based]:<https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb>
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@ -145,23 +145,40 @@ _Chronos_ provides flexible components for forecasting, detection, simulation an
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### **6. Examples and Demos**
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- Quickstarts
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- [Use AutoTSEstimator for Time-Series Forecasting](https://bigdl.readthedocs.io/en/latest/doc/Chronos/QuickStart/chronos-autotsest-quickstart.html)
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- [Use TSDataset and Forecaster for Time-Series Forecasting](https://bigdl.readthedocs.io/en/latest/doc/Chronos/QuickStart/chronos-tsdataset-forecaster-quickstart.html)
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- [Use Anomaly Detector for Unsupervised Anomaly Detection](https://bigdl.readthedocs.io/en/latest/doc/Chronos/QuickStart/chronos-anomaly-detector.html)
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- [Use AutoTSEstimator for Time-Series Forecasting](../QuickStart/chronos-autotsest-quickstart.html)
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- [Use TSDataset and Forecaster for Time-Series Forecasting](../QuickStart/chronos-tsdataset-forecaster-quickstart.html)
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- [Use Anomaly Detector for Unsupervised Anomaly Detection](../QuickStart/chronos-anomaly-detector.html)
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- Examples
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- [Use AutoLSTM on nyc taxi dataset](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/example/auto_model/autolstm_nyc_taxi.py)
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- [Use AutoProphet on nyc taxi dataset](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/example/auto_model/autoprophet_nyc_taxi.py)
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- [High dimension time series forecasting with Chronos TCMFForecaster](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/example/tcmf/run_electricity.py)
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- [Use distributed training with Chronos Seq2SeqForecaster](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/example/distributed/distributed_training_network_traffic.py)
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- [Use ONNXRuntime to accelerate the inference of AutoTSEstimator](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/example/onnx/onnx_autotsestimator_nyc_taxi.py)
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- [Use ONNXRuntime to accelerate the inference of Seq2SeqForecaster](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/example/onnx/onnx_forecaster_network_traffic.py)
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- [Generate synthetic data with DPGANSimulator in a data-driven fashion](https://github.com/intel-analytics/BigDL/tree/branch-2.0/python/chronos/example/simulator)
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- [Use AutoLSTM on nyc taxi dataset][autolstm_nyc_taxi]
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- [Use AutoProphet on nyc taxi dataset][autoprophet_nyc_taxi]
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- [High dimension time series forecasting with Chronos TCMFForecaster][run_electricity]
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- [Use distributed training with Chronos Seq2SeqForecaster][distributed_training_network_traffic]
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- [Use ONNXRuntime to accelerate the inference of AutoTSEstimator][onnx_autotsestimator_nyc_taxi]
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- [Use ONNXRuntime to accelerate the inference of Seq2SeqForecaster][onnx_forecaster_network_traffic]
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- [Generate synthetic data with DPGANSimulator in a data-driven fashion][simulator]
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- Use cases
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- [Unsupervised Anomaly Detection](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb)
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- [Unsupervised Anomaly Detection based on Forecasts](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb)
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- [Stock Price Prediction with LSTM](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction.ipynb)
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- [Stock Price Prediction with ProphetForecaster and AutoProphet](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb)
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- [Network Traffic Forecasting with AutoTSEstimator](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb)
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- [Network Traffic Forecasting (using multivariate time series data)](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb)
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- [Network Traffic Forecasting (using multistep time series data)](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb)
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- [Network Traffic Forecasting with Customized Model](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb)
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- [Unsupervised Anomaly Detection][AIOps_anomaly_detect_unsupervised]
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- [Unsupervised Anomaly Detection based on Forecasts][AIOps_anomaly_detect_unsupervised_forecast_based]
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- [Stock Price Prediction with LSTM][stock_prediction]
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- [Stock Price Prediction with ProphetForecaster and AutoProphet][stock_prediction_prophet]
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- [Network Traffic Forecasting with AutoTSEstimator][network_traffic_autots_forecasting]
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- [Network Traffic Forecasting (using multivariate time series data)][network_traffic_model_forecasting]
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- [Network Traffic Forecasting (using multistep time series data)][network_traffic_multivariate_multistep_tcnforecaster]
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- [Network Traffic Forecasting with Customized Model][network_traffic_autots_customized_model]
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<!--Reference links in article-->
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[autolstm_nyc_taxi]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/auto_model/autolstm_nyc_taxi.py>
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[autoprophet_nyc_taxi]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/auto_model/autoprophet_nyc_taxi.py>
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[run_electricity]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/tcmf/run_electricity.py>
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[distributed_training_network_traffic]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/distributed/distributed_training_network_traffic.py>
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[onnx_autotsestimator_nyc_taxi]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/onnx/onnx_autotsestimator_nyc_taxi.py>
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[onnx_forecaster_network_traffic]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/onnx/onnx_forecaster_network_traffic.py>
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[simulator]: <https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/simulator>
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[AIOps_anomaly_detect_unsupervised]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb>
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[AIOps_anomaly_detect_unsupervised_forecast_based]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb>
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[stock_prediction]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction.ipynb>
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[stock_prediction_prophet]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb>
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[network_traffic_autots_forecasting]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb>
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[network_traffic_model_forecasting]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb>
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[network_traffic_multivariate_multistep_tcnforecaster]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb>
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[network_traffic_autots_customized_model]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb>
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Specify the `name`, the raw data file will be saved in the specified `path` (defaults to ~/.chronos/dataset). `redownload` can help you re-download the files you need.
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When `with_split` is set to True, the length of the data set will be divided according to the specified `val_ratio` and `test_ratio`, and three `TSDataset` will be returned. `with_split` defaults to False, that is, only one `TSDataset` is returned.
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About `TSDataset`, more details, please refer to [here](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/tsdataset.html).
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About `TSDataset`, more details, please refer to [here](../../PythonAPI/Chronos/tsdataset.html).
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```python
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# load built-in dataset
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@ -73,7 +73,7 @@ For AutoTS Pipeline, we will leverage `AutoTSEstimator`, `TSPipeline` and prefer
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.. note::
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`AutoTSEstimator` currently only support pytorch backend.
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```
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View [Quick Start](https://bigdl.readthedocs.io/en/latest/doc/Chronos/QuickStart/chronos-autotsest-quickstart.html) for a more detailed example.
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View [Quick Start](../QuickStart/chronos-autotsest-quickstart.html) for a more detailed example.
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##### **2.1 Prepare dataset**
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`AutoTSEstimator` support 2 types of data input.
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batch_size=hp.randint(32, 64),
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epochs=5)
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```
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Detailed information and settings please refer to [AutoTSEstimator API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/autotsestimator.html#id1).
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Detailed information and settings please refer to [AutoTSEstimator API doc](../../PythonAPI/Chronos/autotsestimator.html#id1).
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##### **2.4 Development on TSPipeline**
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You may carry out predict, evaluate, incremental training or save/load for further development.
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```python
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@ -157,7 +157,7 @@ Detailed information please refer to [TSPipeline API doc](../../PythonAPI/Chrono
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_Chronos_ provides a set of standalone time series forecasters without AutoML support, including deep learning models as well as traditional statistical models.
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View some examples notebooks for [Network Traffic Prediction](https://github.com/intel-analytics/BigDL/tree/branch-2.0/python/chronos/use-case/network_traffic)
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View some examples notebooks for [Network Traffic Prediction][network_traffic]
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The common process of using a Forecaster looks like below.
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```python
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LSTMForecaster wraps a vanilla LSTM model, and is suitable for univariate time series forecasting.
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View Network Traffic Prediction [notebook](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb) and [LSTMForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#lstmforecaster) for more details.
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View Network Traffic Prediction [notebook][network_traffic_model_forecasting] and [LSTMForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#lstmforecaster) for more details.
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<span id="Seq2SeqForecaster"></span>
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###### **3.2 Seq2SeqForecaster**
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Temporal Convolutional Networks (TCN) is a neural network that use convolutional architecture rather than recurrent networks. It supports multi-step and multi-variant cases. Causal Convolutions enables large scale parallel computing which makes TCN has less inference time than RNN based model such as LSTM.
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View Network Traffic multivariate multistep Prediction [notebook](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb) and [TCNForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#tcnforecaster) for more details.
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View Network Traffic multivariate multistep Prediction [notebook][network_traffic_multivariate_multistep_tcnforecaster] and [TCNForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#tcnforecaster) for more details.
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<span id="MTNetForecaster"></span>
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###### **3.4 MTNetForecaster**
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MTNetForecaster wraps a MTNet model. The model architecture mostly follows the [MTNet paper](https://arxiv.org/abs/1809.02105) with slight modifications, and is suitable for multivariate time series forecasting.
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View Network Traffic Prediction [notebook](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb) and [MTNetForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#mtnetforecaster) for more details.
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View Network Traffic Prediction [notebook][network_traffic_model_forecasting] and [MTNetForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#mtnetforecaster) for more details.
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<span id="TCMFForecaster"></span>
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###### **3.5 TCMFForecaster**
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TCMFForecaster wraps a model architecture that follows implementation of the paper [DeepGLO paper](https://arxiv.org/abs/1905.03806) with slight modifications. It is especially suitable for extremely high dimensional (up-to millions) multivariate time series forecasting.
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View High-dimensional Electricity Data Forecasting [example](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/example/tcmf/run_electricity.py) and [TCMFForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#tcmfforecaster) for more details.
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View High-dimensional Electricity Data Forecasting [example][run_electricity] and [TCMFForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#tcmfforecaster) for more details.
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<span id="ARIMAForecaster"></span>
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###### **3.6 ARIMAForecaster**
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ProphetForecaster wraps the Prophet model ([site](https://github.com/facebook/prophet)) which is an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects and is suitable for univariate time series forecasting. It works best with time series that have strong seasonal effects and several seasons of historical data and is robust to missing data and shifts in the trend, and typically handles outliers well.
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View Stock Prediction [notebook](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb) and [ProphetForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#prophetforecaster) for more details.
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View Stock Prediction [notebook][stock_prediction_prophet] and [ProphetForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#prophetforecaster) for more details.
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<span id="NBeatsForecaster"></span>
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###### **3.8 NBeatsForecaster**
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# input test data x, batch size...
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auto_model.predict(...)
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```
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The input data can be easily get from `TSDataset`. Users can refer to detailed [API doc](../../PythonAPI/Chronos/automodels.html).
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The input data can be easily get from `TSDataset`. Users can refer to detailed [API doc](../../PythonAPI/Chronos/automodels.html).
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[network_traffic]:<https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/network_traffic>
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[network_traffic_model_forecasting]:<https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb>
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[network_traffic_multivariate_multistep_tcnforecaster]:<https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb>
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[run_electricity]:<https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/tcmf/run_electricity.py>
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[stock_prediction_prophet]:<https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb>
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## **1. DPGANSimulator**
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`DPGANSimulator` adopt DoppelGANger raised in [Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions](http://arxiv.org/abs/1909.13403). The method is data-driven unsupervised method based on deep learning model with GAN (Generative Adversarial Networks) structure. The model features a pair of seperate attribute generator and feature generator and their corresponding discriminators `DPGANSimulator` also supports a rich and comprehensive input data (training data) format and outperform other algorithms in many evalution metrics.
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Users may refer to detailed [API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/simulator.html#module-bigdl.chronos.simulator.doppelganger_simulator).
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Users may refer to detailed [API doc](../../PythonAPI/Chronos/simulator.html#module-bigdl.chronos.simulator.doppelganger_simulator).
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@ -2,7 +2,7 @@
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---
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[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb)
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[Run in Google Colab][chronos_minn_traffic_anomaly_detector_colab] [View source on GitHub][chronos_minn_traffic_anomaly_detector]
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---
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```
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## Step 2: Use Chronos Anomaly Detector
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Chronos provides many anomaly detector for anomaly detection, here we use DBScan as an example. More anomaly detector can be found [here](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/anomaly_detectors.html).
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Chronos provides many anomaly detector for anomaly detection, here we use DBScan as an example. More anomaly detector can be found [here](../../PythonAPI/Chronos/anomaly_detectors.html).
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|
||||
```python
|
||||
from bigdl.chronos.detector.anomaly import DBScanDetector
|
||||
|
|
@ -46,4 +46,5 @@ ad.fit(df['value'].to_numpy())
|
|||
anomaly_indexes = ad.anomaly_indexes()
|
||||
```
|
||||
|
||||
|
||||
[chronos_minn_traffic_anomaly_detector_colab]: <https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb>
|
||||
[chronos_minn_traffic_anomaly_detector]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb>
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
---
|
||||
|
||||
[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb)
|
||||
[Run in Google Colab][chronos_autots_nyc_taxi_colab] [View source on GitHub][chronos_autots_nyc_taxi]
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -56,7 +56,7 @@ There is no need to call `.roll()` or `.to_torch_data_loader()` in this step, wh
|
|||
|
||||
Please call `.gen_dt_feature()`(recommended), `.gen_rolling_feature()`, and `gen_global_feature()` to generate all candidate features to be selected by `AutoTSEstimator` as well as your input extra feature.
|
||||
|
||||
Detailed information please refer to [TSDataset API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/tsdataset.html) and [Time series data basic concepts](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/data_processing_feature_engineering.html).
|
||||
Detailed information please refer to [TSDataset API doc](../../PythonAPI/Chronos/tsdataset.html) and [Time series data basic concepts](../Overview/data_processing_feature_engineering.html).
|
||||
|
||||
### **Step 3: Create an AutoTSEstimator**
|
||||
|
||||
|
|
@ -72,7 +72,7 @@ We prebuild three defualt search space for each build-in model, which you can us
|
|||
|
||||
`past_seq_len` can be set as a hp sample function, the proper range is highly related to your data. A range between 0.5 cycle and 3 cycle is reasonable.
|
||||
|
||||
Detailed information please refer to [AutoTSEstimator API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/autotsestimator.html#autotsestimator) and basic concepts [here](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/forecasting.html#use-autots-pipeline).
|
||||
Detailed information please refer to [AutoTSEstimator API doc](../../PythonAPI/Chronos/autotsestimator.html#autotsestimator) and basic concepts [here](../Overview/forecasting.html#use-autots-pipeline).
|
||||
|
||||
### **Step 4: Fit with AutoTSEstimator**
|
||||
```python
|
||||
|
|
@ -81,7 +81,7 @@ ts_pipeline = auto_estimator.fit(data=tsdata_train, # train dataset
|
|||
validation_data=tsdata_val, # validation dataset
|
||||
epochs=5) # number of epochs to train in each trial
|
||||
```
|
||||
Detailed information please refer to [AutoTSEstimator API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/autotsestimator.html#autotsestimator).
|
||||
Detailed information please refer to [AutoTSEstimator API doc](../../PythonAPI/Chronos/autotsestimator.html#autotsestimator).
|
||||
### **Step 5: Further deployment with TSPipeline**
|
||||
The `TSPipeline` will reply the same preprcessing and corresponding postprocessing operations on the test data. You may carry out predict, evaluate or save/load for further development.
|
||||
```python
|
||||
|
|
@ -104,7 +104,7 @@ ts_pipeline.save(my_ppl_file_path)
|
|||
from bigdl.chronos.autots import TSPipeline
|
||||
loaded_ppl = TSPipeline.load(my_ppl_file_path)
|
||||
```
|
||||
Detailed information please refer to [TSPipeline API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/tsdataset.html).
|
||||
Detailed information please refer to [TSPipeline API doc](../../PythonAPI/Chronos/tsdataset.html).
|
||||
|
||||
### **Optional: Examine the leaderboard visualization**
|
||||
To view the evaluation result of "not chosen" trails and find some insight or even possibly improve you search space for a new autotuning task. We provide a leaderboard through tensorboard.
|
||||
|
|
@ -113,4 +113,7 @@ To view the evaluation result of "not chosen" trails and find some insight or ev
|
|||
%load_ext tensorboard
|
||||
%tensorboard --logdir /tmp/autots_estimator/autots_estimator_leaderboard/
|
||||
```
|
||||
Detailed information please refer to [Visualization](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/useful_functionalities.html#automl-visualization).
|
||||
Detailed information please refer to [Visualization](../Overview/useful_functionalities.html#automl-visualization).
|
||||
|
||||
[chronos_autots_nyc_taxi_colab]: <https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb>
|
||||
[chronos_autots_nyc_taxi]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb>
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
---
|
||||
|
||||
[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb)
|
||||
[Run in Google Colab][chronos_nyc_taxi_tsdataset_forecaster_colab] [View source on GitHub][chronos_nyc_taxi_tsdataset_forecaster]
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -20,7 +20,7 @@ pip install bigdl-chronos[all]
|
|||
|
||||
### Step 1: Data transformation and feature engineering using Chronos TSDataset
|
||||
|
||||
[TSDataset](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/data_processing_feature_engineering.html) is our abstract of time series dataset for data transformation and feature engineering. Here we use it to preprocess the data.
|
||||
[TSDataset](../Overview/data_processing_feature_engineering.html) is our abstract of time series dataset for data transformation and feature engineering. Here we use it to preprocess the data.
|
||||
|
||||
Initialize train, valid and test tsdataset from raw pandas dataframe.
|
||||
|
||||
|
|
@ -54,7 +54,7 @@ for tsdata in [tsdata_train, tsdata_valid, tsdata_test]:
|
|||
|
||||
### Step 2: Time series forecasting using Chronos Forecaster
|
||||
|
||||
After preprocessing the datasets. We can use [Chronos Forecaster](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/forecasting.html#use-standalone-forecaster-pipeline) to handle the forecasting tasks.
|
||||
After preprocessing the datasets. We can use [Chronos Forecaster](../Overview/forecasting.html#use-standalone-forecaster-pipeline) to handle the forecasting tasks.
|
||||
|
||||
Transform TSDataset to sampled numpy ndarray and feed them to forecaster.
|
||||
|
||||
|
|
@ -87,3 +87,6 @@ Save & restore the forecaster.
|
|||
forecaster.save("nyc_taxi.fxt")
|
||||
forecaster.restore("nyc_taxi.fxt")
|
||||
```
|
||||
|
||||
[chronos_nyc_taxi_tsdataset_forecaster_colab]:<https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb>
|
||||
[chronos_nyc_taxi_tsdataset_forecaster]:<https://github.com/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb>
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
- [**Predict Number of Taxi Passengers with Chronos Forecaster**](./chronos-tsdataset-forecaster-quickstart.html)
|
||||
|
||||
> [Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb)
|
||||
> [Run in Google Colab][chronos_nyc_taxi_tsdataset_forecaster_colab] [View source on GitHub][chronos_nyc_taxi_tsdataset_forecaster]
|
||||
|
||||
In this guide we will demonstrate how to use _Chronos TSDataset_ and _Chronos Forecaster_ for time series processing and predict number of taxi passengers.
|
||||
|
||||
|
|
@ -10,7 +10,7 @@
|
|||
|
||||
- [**Tune a Forecasting Task Automatically**](./chronos-autotsest-quickstart.html)
|
||||
|
||||
> [Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb)
|
||||
> [Run in Google Colab][chronos_autots_nyc_taxi_colab] [View source on GitHub][chronos_autots_nyc_taxi]
|
||||
|
||||
In this guide we will demonstrate how to use _Chronos AutoTSEstimator_ and _Chronos TSPipeline_ to auto tune a time seires forecasting task and handle the whole model development process easily.
|
||||
|
||||
|
|
@ -18,47 +18,47 @@
|
|||
|
||||
- [**Detect Anomaly Point in Real Time Traffic Data**](./chronos-anomaly-detector.html)
|
||||
|
||||
> [Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb)
|
||||
> [Run in Google Colab][chronos_minn_traffic_anomaly_detector_colab] [View source on GitHub][chronos_minn_traffic_anomaly_detector]
|
||||
|
||||
In this guide we will demonstrate how to use _Chronos Anomaly Detector_ for real time traffic data from the Twin Cities Metro area in Minnesota anomaly detection.
|
||||
|
||||
---------------------------
|
||||
|
||||
- [**Tune a Customized Time Series Forecasting Model with AutoTSEstimator.**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb)
|
||||
- [**Tune a Customized Time Series Forecasting Model with AutoTSEstimator.**][network_traffic_autots_customized_model]
|
||||
|
||||
> [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb)
|
||||
> [View source on GitHub][network_traffic_autots_customized_model]
|
||||
|
||||
In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demonstrate how to use _AutoTSEstimator_ to adjust the parameters of a customized model.
|
||||
|
||||
---------------------------
|
||||
|
||||
- [**Auto Tune the Prediction of Network Traffic at the Transit Link of WIDE**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb)
|
||||
- [**Auto Tune the Prediction of Network Traffic at the Transit Link of WIDE**][network_traffic_autots_forecasting]
|
||||
|
||||
> [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb)
|
||||
> [View source on GitHub][network_traffic_autots_forecasting]
|
||||
|
||||
In this notebook, we demostrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to use _AutoTS_ in project [Chronos][4] to do time series forecasting in an automated and distributed way.
|
||||
In this notebook, we demostrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to use _AutoTS_ in project [Chronos][chronos] to do time series forecasting in an automated and distributed way.
|
||||
|
||||
---------------------------
|
||||
|
||||
- [**Multivariate Forecasting of Network Traffic at the Transit Link of WIDE**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb)
|
||||
- [**Multivariate Forecasting of Network Traffic at the Transit Link of WIDE**][network_traffic_model_forecasting]
|
||||
|
||||
> [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb)
|
||||
> [View source on GitHub][network_traffic_model_forecasting]
|
||||
|
||||
In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to do univariate forecasting (predict only 1 series), and multivariate forecasting (predicts more than 1 series at the same time) using Project [Chronos][4].
|
||||
In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to do univariate forecasting (predict only 1 series), and multivariate forecasting (predicts more than 1 series at the same time) using Project [Chronos][chronos].
|
||||
|
||||
---------------------------
|
||||
|
||||
- [**Multistep Forecasting of Network Traffic at the Transit Link of WIDE**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb)
|
||||
- [**Multistep Forecasting of Network Traffic at the Transit Link of WIDE**][network_traffic_multivariate_multistep_tcnforecaster]
|
||||
|
||||
> [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb)
|
||||
> [View source on GitHub][network_traffic_multivariate_multistep_tcnforecaster]
|
||||
|
||||
In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to do multivariate multistep forecasting using Project [Chronos][4].
|
||||
In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to do multivariate multistep forecasting using Project [Chronos][chronos].
|
||||
|
||||
---------------------------
|
||||
|
||||
- [**Stock Price Prediction with LSTMForecaster**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction.ipynb)
|
||||
- [**Stock Price Prediction with LSTMForecaster**][stock_prediction]
|
||||
|
||||
> [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction.ipynb)
|
||||
> [View source on GitHub][stock_prediction]
|
||||
|
||||
In this notebook, we demonstrate a reference use case where we use historical stock price data to predict the future price. The dataset we use is the daily stock price of S&P500 stocks during 2013-2018 (data source). We demostrate how to do univariate forecasting using the past 80% of the total days' MMM price to predict the future 20% days' daily price.
|
||||
|
||||
|
|
@ -66,9 +66,9 @@
|
|||
|
||||
---------------------------
|
||||
|
||||
- [**Stock Price Prediction with ProphetForecaster and AutoProphet**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb)
|
||||
- [**Stock Price Prediction with ProphetForecaster and AutoProphet**][stock_prediction_prophet]
|
||||
|
||||
> [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb)
|
||||
> [View source on GitHub][stock_prediction_prophet]
|
||||
|
||||
In this notebook, we demonstrate a reference use case where we use historical stock price data to predict the future price using the ProphetForecaster and AutoProphet. The dataset we use is the daily stock price of S&P500 stocks during 2013-2018 [data source](https://www.kaggle.com/camnugent/sandp500/).
|
||||
|
||||
|
|
@ -76,23 +76,36 @@
|
|||
|
||||
---------------------------
|
||||
|
||||
- [**Unsupervised Anomaly Detection for CPU Usage**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb)
|
||||
- [**Unsupervised Anomaly Detection for CPU Usage**][AIOps_anomaly_detect_unsupervised]
|
||||
|
||||
> [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb)
|
||||
> [View source on GitHub][AIOps_anomaly_detect_unsupervised]
|
||||
|
||||
We demonstrates how to perform anomaly detection based on Chronos's built-in [DBScanDetector][DBScan], [AEDetector][AE] and [ThresholdDetector][Threshold].
|
||||
|
||||
---------------------------
|
||||
|
||||
- [**Anomaly Detection for CPU Usage Based on Forecasters**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb)
|
||||
- [**Anomaly Detection for CPU Usage Based on Forecasters**][AIOps_anomaly_detect_unsupervised_forecast_based]
|
||||
|
||||
> [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb)
|
||||
> [View source on GitHub][AIOps_anomaly_detect_unsupervised_forecast_based]
|
||||
|
||||
We demonstrates how to leverage Chronos's built-in models ie. MTNet, to do time series forecasting. Then perform anomaly detection on predicted value with [ThresholdDetector][Threshold].
|
||||
|
||||
|
||||
[DBScan]: <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/anomaly_detectors.html#dbscandetector>
|
||||
[AE]: <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/anomaly_detectors.html#aedetector>
|
||||
[Threshold]: <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/anomaly_detectors.html#thresholddetector>
|
||||
[4]: <https://github.com/intel-analytics/bigdl/tree/branch-2.0/python/chronos/src/bigdl/chronos>
|
||||
|
||||
[DBScan]: <../../PythonAPI/Chronos/anomaly_detectors.html#dbscandetector>
|
||||
[AE]: <../../PythonAPI/Chronos/anomaly_detectors.html#aedetector>
|
||||
[Threshold]: <../../PythonAPI/Chronos/anomaly_detectors.html#thresholddetector>
|
||||
[chronos]: <https://github.com/intel-analytics/bigdl/tree/main/python/chronos/src/bigdl/chronos>
|
||||
[chronos_nyc_taxi_tsdataset_forecaster_colab]: <https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb>
|
||||
[chronos_nyc_taxi_tsdataset_forecaster]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb>
|
||||
[chronos_autots_nyc_taxi_colab]: <https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb>
|
||||
[chronos_autots_nyc_taxi]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb>
|
||||
[chronos_minn_traffic_anomaly_detector_colab]: <https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb>
|
||||
[chronos_minn_traffic_anomaly_detector]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb>
|
||||
[network_traffic_autots_customized_model]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb>
|
||||
[network_traffic_autots_forecasting]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb>
|
||||
[network_traffic_model_forecasting]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb>
|
||||
[network_traffic_multivariate_multistep_tcnforecaster]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb>
|
||||
[stock_prediction]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction.ipynb>
|
||||
[stock_prediction_prophet]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb>
|
||||
[AIOps_anomaly_detect_unsupervised]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb>
|
||||
[AIOps_anomaly_detect_unsupervised_forecast_based]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb>
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@ -8,7 +8,7 @@ LSTMForecaster
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Long short-term memory(LSTM) is a special type of recurrent neural network(RNN). We implement the basic version of LSTM - VanillaLSTM for this forecaster for time-series forecasting task. It has two LSTM layers, two dropout layer and a dense layer.
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For the detailed algorithm description, please refer to `here <https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/Chronos/Algorithm/LSTMAlgorithm.md>`__.
|
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For the detailed algorithm description, please refer to `here <https://github.com/intel-analytics/BigDL/blob/main/docs/docs/Chronos/Algorithm/LSTMAlgorithm.md>`__.
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.. automodule:: bigdl.chronos.forecaster.lstm_forecaster
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:members:
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@ -67,7 +67,7 @@ TCMFForecaster supports distributed training and inference. It is based on Orca
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**Remarks**:
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* You can refer to `TCMFForecaster installation <https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/Chronos/tutorials/TCMFForecaster.md#step-0-prepare-environment>`__ to install required packages.
|
||||
* You can refer to `TCMFForecaster installation <https://github.com/intel-analytics/BigDL/blob/main/docs/docs/Chronos/tutorials/TCMFForecaster.md#step-0-prepare-environment>`__ to install required packages.
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||||
* Your operating system (OS) is required to be one of the following 64-bit systems: **Ubuntu 16.04 or later** and **macOS 10.12.6 or later**.
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.. automodule:: bigdl.chronos.forecaster.tcmf_forecaster
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@ -81,9 +81,9 @@ MTNetForecaster
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MTNet is a memory-network based solution for multivariate time-series forecasting. In a specific task of multivariate time-series forecasting, we have several variables observed in time series and we want to forecast some or all of the variables' value in a future time stamp.
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MTNet is proposed by paper `A Memory-Network Based Solution for Multivariate Time-Series Forecasting <https://arxiv.org/abs/1809.02105>`__. MTNetForecaster is derived from tfpark.KerasMode, and can use all methods of KerasModel. Refer to `tfpark.KerasModel API Doc <https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/APIGuide/TFPark/model.md>`__ for details.
|
||||
MTNet is proposed by paper `A Memory-Network Based Solution for Multivariate Time-Series Forecasting <https://arxiv.org/abs/1809.02105>`__. MTNetForecaster is derived from tfpark.KerasMode, and can use all methods of KerasModel. Refer to `tfpark.KerasModel API Doc <https://github.com/intel-analytics/BigDL/blob/main/docs/docs/APIGuide/TFPark/model.md>`__ for details.
|
||||
|
||||
For the detailed algorithm description, please refer to `here <https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/Chronos/Algorithm/MTNetAlgorithm.md>`__.
|
||||
For the detailed algorithm description, please refer to `here <https://github.com/intel-analytics/BigDL/blob/main/docs/docs/Chronos/Algorithm/MTNetAlgorithm.md>`__.
|
||||
|
||||
.. automodule:: bigdl.chronos.forecaster.mtnet_forecaster
|
||||
:members:
|
||||
|
|
|
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Reference in a new issue