# Chronos Tutorial - [**Predict Number of Taxi Passengers with Chronos Forecaster**](./chronos-tsdataset-forecaster-quickstart.html) > ![](../../../../image/colab_logo_32px.png)[Run in Google Colab][chronos_nyc_taxi_tsdataset_forecaster_colab]  ![](../../../../image/GitHub-Mark-32px.png)[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. --------------------------- - [**Tune a Forecasting Task Automatically**](./chronos-autotsest-quickstart.html) > ![](../../../../image/colab_logo_32px.png)[Run in Google Colab][chronos_autots_nyc_taxi_colab]  ![](../../../../image/GitHub-Mark-32px.png)[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. --------------------------- - [**Detect Anomaly Point in Real Time Traffic Data**](./chronos-anomaly-detector.html) > ![](../../../../image/colab_logo_32px.png)[Run in Google Colab][chronos_minn_traffic_anomaly_detector_colab]  ![](../../../../image/GitHub-Mark-32px.png)[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.**][network_traffic_autots_customized_model] > ![](../../../../image/GitHub-Mark-32px.png)[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**][network_traffic_autots_forecasting] > ![](../../../../image/GitHub-Mark-32px.png)[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][chronos] to do time series forecasting in an automated and distributed way. --------------------------- - [**Multivariate Forecasting of Network Traffic at the Transit Link of WIDE**][network_traffic_model_forecasting] > ![](../../../../image/GitHub-Mark-32px.png)[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][chronos]. --------------------------- - [**Multistep Forecasting of Network Traffic at the Transit Link of WIDE**][network_traffic_multivariate_multistep_tcnforecaster] > ![](../../../../image/GitHub-Mark-32px.png)[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][chronos]. --------------------------- - [**Stock Price Prediction with LSTMForecaster**][stock_prediction] > ![](../../../../image/GitHub-Mark-32px.png)[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. Reference: ** --------------------------- - [**Stock Price Prediction with ProphetForecaster and AutoProphet**][stock_prediction_prophet] > ![](../../../../image/GitHub-Mark-32px.png)[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/). Reference: **, ** --------------------------- - [**Unsupervised Anomaly Detection for CPU Usage**][AIOps_anomaly_detect_unsupervised] > ![](../../../../image/GitHub-Mark-32px.png)[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**][AIOps_anomaly_detect_unsupervised_forecast_based] > ![](../../../../image/GitHub-Mark-32px.png)[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]. --------------------------- - [**Help pytorch-forecasting improve the training speed of DeepAR model**][pytorch_forecasting_deepar] > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][pytorch_forecasting_deepar] 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**][pytorch_forecasting_tft] > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][pytorch_forecasting_tft] 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. [DBScan]: <../../PythonAPI/Chronos/anomaly_detectors.html#dbscandetector> [AE]: <../../PythonAPI/Chronos/anomaly_detectors.html#aedetector> [Threshold]: <../../PythonAPI/Chronos/anomaly_detectors.html#thresholddetector> [chronos]: [chronos_nyc_taxi_tsdataset_forecaster_colab]: [chronos_nyc_taxi_tsdataset_forecaster]: [chronos_autots_nyc_taxi_colab]: [chronos_autots_nyc_taxi]: [chronos_minn_traffic_anomaly_detector_colab]: [chronos_minn_traffic_anomaly_detector]: [network_traffic_autots_customized_model]: [network_traffic_autots_forecasting]: [network_traffic_model_forecasting]: [network_traffic_multivariate_multistep_tcnforecaster]: [stock_prediction]: [stock_prediction_prophet]: [AIOps_anomaly_detect_unsupervised]: [AIOps_anomaly_detect_unsupervised_forecast_based]: [pytorch_forecasting_deepar]: [pytorch_forecasting_tft]: