# Chronos Tutorial
```eval_rst .. raw:: html

Filter:

Please check the checkboxes or click tag buttons to show the related tutorials. Reclick or uncheck will hide corresponding tutorials. If nothing is checked or clicked, all the tutorials will be displayed.

forecast
anomaly detection
simulation
AutoML
onnxruntime
quantization
distributed
customized model

Predict Number of Taxi Passengers with Chronos Forecaster

Tag:

Run in Google Colab   View source on GitHub

In this guide we will demonstrate how to use Chronos TSDataset and Chronos Forecaster for time series processing and predict number of taxi passengers.

Tune a Forecasting Task Automatically

Tag:  

 Run in Google Colab   View source on GitHub

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.

Detect Anomaly Point in Real Time Traffic Data

Tag:

Run in Google Colab   View source on GitHub

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.

Tag:   

 View source on GitHub

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

Tag:  

 View source on GitHub

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 to do time series forecasting in an automated and distributed way.

Multivariate Forecasting of Network Traffic at the Transit Link of WIDE

Tag:

View source on GitHub

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.

Multistep Forecasting of Network Traffic at the Transit Link of WIDE

Tag:

View source on GitHub

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.

Stock Price Prediction with LSTMForecaster

Tag:

View source on GitHub

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.

Reference: https://github.com/jwkanggist/tf-keras-stock-pred

Stock Price Prediction with ProphetForecaster and AutoProphet

Tag:  

 View source on GitHub

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.

Reference: https://facebook.github.io/prophet, https://github.com/jwkanggist/tf-keras-stock-pred

Unsupervised Anomaly Detection for CPU Usage

Tag:

View source on GitHub

We demonstrates how to perform anomaly detection based on Chronos's built-in DBScanDetector, AEDetector and ThresholdDetector.

Anomaly Detection for CPU Usage Based on Forecasters

Tag:  

 View source on GitHub

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.

Help pytorch-forecasting improve the training speed of DeepAR model

Tag:  

 View source on GitHub

Chronos can help a 3rd party time series lib to improve the performance (both training and inferencing) and accuracy. This use-case shows Chronos can easily help pytorch-forecasting speed up the training of DeepAR model.

Help pytorch-forecasting improve the training speed of TFT model

Tag:  

 View source on GitHub

Chronos can help a 3rd party time series lib to improve the performance (both training and inferencing) and accuracy. This use-case shows Chronos can easily help pytorch-forecasting speed up the training of TFT model.

Tune a Time Series Forecasting Model with multi-objective hyperparameter optimization.

Tag:  

 View source on GitHub

In this notebook, we demostrate how to use multi-objective hyperparameter optimization with built-in latency metric in project Chronos to do time series forecasting and achieve good tradeoff between performance and latency.

Auto tuning prophet on nyc taxi dataset

Tag:  

 View source on GitHub

This example collection will demonstrate Chronos auto models (i.e. autolstm & autoprophet) perform automatic time series forecasting on nyc_taxi dataset. The auto model will search the best hyperparameters automatically.

Use Chronos forecasters in a distributed fashion

Tag:  

 View source on GitHub

Users can easily train their forecasters in a distributed fashion to handle extra large dataset and speed up the process (training and data processing) by utilizing a cluster or pseudo-distribution on a single node. The functionality is powered by Project Orca.

Use ONNXRuntime to speed-up forecasters' inferecing

Tag:   

 View source on GitHub

This example will demonstrate how to use ONNX to speed up the inferencing(prediction/evalution) on forecasters and AutoTSEstimator. In this example, onnx speed up the inferencing for ~4X.

Quantize Chronos forecasters method to speed-up inference

Tag:  

 View source on GitHub

Users can easily quantize their forecasters to low precision and speed up the inference process (both throughput and latency) by on a single node. The functionality is powered by Project Nano.

Simualate time series data with similar pattern as example data

Tag:

View source on GitHub

This example shows how to generate synthetic data with similar distribution as training data with the fast and easy DPGANSimulator API provided by Chronos.

High dimension time series forecasting with Chronos TCMFForecaster

Tag:  

 View source on GitHub

This example demonstrates how to use BigDL Chronos TCMFForecaster to run distributed training and inference for high dimension time series forecasting task.

Penalize underestimation with LinexLoss

Tag:

View source on GitHub

This example demonstrates how to use TCNForecaster to penalize underestimation based on a built-in loss function LinexLoss.

```