# Chronos Examples ```eval_rst .. raw:: html
Please check the checkboxes or click tag buttons to show the related examples. Reclick or uncheck will hide corresponding examples. If nothing is checked or clicked, all the examples will be displayed.
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In this guide we will demonstrate how to use Chronos TSDataset and Chronos Forecaster for time series processing and predict number of taxi passengers.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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
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We demonstrates how to perform anomaly detection based on Chronos's built-in DBScanDetector, AEDetector and ThresholdDetector.
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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.
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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.
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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.
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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.
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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.
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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.
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This example will demonstrate how to use ONNX to speed up the inferencing(prediction/evaluation) on forecasters and AutoTSEstimator. In this example, onnx speed up the inferencing for ~4X.
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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.
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This example shows how to generate synthetic data with similar distribution as training data with the fast and easy DPGANSimulator API provided by Chronos.
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This example demonstrates how to use BigDL Chronos TCMFForecaster to run distributed training and inference for high dimension time series forecasting task.
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This example demonstrates how to use TCNForecaster to penalize underestimation based on a built-in loss function LinexLoss.
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In this example, we show an example to train the model on GPU and accelerate the model by using onnxruntime on CPU.
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In this example, we show how to serve Chronos forecaster and predict through TorchServe.