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Chronos Tutorial
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Predict Number of Taxi Passengers with Chronos 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.
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Tune a Forecasting Task Automatically
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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Detect Anomaly Point in Real Time Traffic Data
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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Tune a Customized Time Series Forecasting Model with AutoTSEstimator.
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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Auto Tune the Prediction of Network Traffic at the Transit Link of WIDE
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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Multivariate Forecasting of Network Traffic at the Transit Link of WIDE
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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Multistep Forecasting of Network Traffic at the Transit Link of WIDE
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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Stock Price Prediction with LSTMForecaster
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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Stock Price Prediction with ProphetForecaster and AutoProphet
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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Unsupervised Anomaly Detection for CPU Usage
We demonstrates how to perform anomaly detection based on Chronos's built-in DBScanDetector, AEDetector and ThresholdDetector.
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Anomaly Detection for CPU Usage Based on Forecasters
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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Help pytorch-forecasting improve the training speed of DeepAR model
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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Help pytorch-forecasting improve the training speed of TFT model
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.