ayo/ipex-llm
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 1

Chronos: update js, css and md files to add filter function in tutorial page. (#5506)

Browse source
This commit is contained in:
Juntao_Luo 2022-08-23 12:50:29 +08:00 committed by GitHub
parent c94e934068
commit 584ee6045c
3 changed files with 439 additions and 111 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

51
docs/readthedocs/source/_static/css/chronos_tutorial.css Normal file
View file

@ -0,0 +1,51 @@
#tutorial img{
margin: 10px 5px 10px 15px;
}
.choiceline{
display: flex;
justify-content: space-around;
margin: 0px 5px 10px 5px;
}
.choicebox{
width: 200px;
height: 20px;
}
summary p{
margin: 10px 0 0 0;
font-weight: bold;
}
details p{
padding: 5px 15px;
color: #404040;
}
.border{
background-color: #f3f6f6;
border: 1px solid rgba(0,0,0,.1);
color: #404040;
box-shadow: inset 0 1px 2px -1px hsl(0deg 0% 100% / 50%), inset 0 -2px 0 0 rgb(0 0 0 / 10%);
margin-bottom: 10px;
padding-top: 10px;
padding-left: 5px;
padding-right: 5px;
margin-top: -10px;
border-radius: 5px;
font-weight: bold;
}
details p span{
font-style: italic;
font-weight: bold;
}
summary p button{
border-radius: 2px;
border: 1px solid rgba(0,0,0,.1);
box-shadow: inset 0 1px 2px -1px hsl(0deg 0% 100% / 90%);
color: #404040;
font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;
}

175
docs/readthedocs/source/_static/js/chronos_tutorial.js Normal file
View file

@ -0,0 +1,175 @@
$(document).ready(function(){
$("#tutorial details").attr("open",true);
});
//func to show a tutorial
function showTutorials(ids){
ids.forEach(id => {
$("#"+id).css("display","block");
$("#"+id).attr("open","true");
$("#"+id).next().css("display","block");
});
}
//func to disable checkbox and button
function disCheck(ids){
ids.forEach(id => {
$("#"+id).prop("disabled", true);
$("#"+id).parent().css("color","#c5c5c5");
$("button[value='"+id+"']").prop("disabled",true);
$("button[value='"+id+"']").css("color","#c5c5c5");
});
}
//event when click the checkboxes
$(".checkboxes").click(function(){
//get all checked values
var vals = [];
$('input:checkbox:checked').each(function (index, item) {
vals.push($(this).val());
});
//reset display
$("#tutorial details").css("display","none");
$("#tutorial hr").css("display","none");
//reset checkbox and button
$("#tutorial button").prop("disabled",false);
$("#tutorial input[type='checkbox']").prop("disabled",false);
$("#tutorial input[type='checkbox']").parent().css("color","#404040");
$("#tutorial button").css("color","#404040");
//show tutorial according to checked values
if(vals.length==0){
//choose noting, show all tutorials
$("#tutorial details").css("display","block");
$("#tutorial details").attr("open",true);
$("#tutorial hr").css("display","block");
}
//chose something, disable invalid checkboxes and buttons accordingly.
else if(vals.length==1){
if(vals.includes("forecast")){
var ids = ["ChronosForecaster","TuneaForecasting","AutoTSEstimator","AutoWIDE",
"MultvarWIDE","MultstepWIDE","LSTMForecaster","AutoProphet","AnomalyDetection",
"DeepARmodel","TFTmodel","hyperparameter","taxiDataset","distributedFashion",
"ONNX","Quantize","TCMFForecaster"];
showTutorials(ids);
var disIds = ["simulation"];
disCheck(disIds);
}
else if(vals.includes("anomaly_detection")){
var ids = ["DetectAnomaly","Unsupervised","AnomalyDetection"];
showTutorials(ids);
var disIds = ["simulation","hyperparameter_tuning","onnxruntime","quantization","distributed","customized_model"];
disCheck(disIds);
}
else if(vals.includes("simulation")){
var ids = ["SimualateTimeSeriesData"];
showTutorials(ids);
var disIds = ["forecast","anomaly_detection","hyperparameter_tuning","onnxruntime","quantization","distributed","customized_model"];
disCheck(disIds);
}
else if(vals.includes("hyperparameter_tuning")){
var ids = ["TuneaForecasting","AutoTSEstimator","AutoWIDE","AutoProphet",
"hyperparameter","taxiDataset","ONNX"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","quantization","distributed"];
disCheck(disIds);
}
else if(vals.includes("onnxruntime")){
var ids = ["ONNX"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","quantization","distributed","customized_model"];
disCheck(disIds);
}
else if(vals.includes("quantization")){
var ids = ["Quantize"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","distributed","customized_model"];
disCheck(disIds);
}
else if(vals.includes("distributed")){
var ids = ["distributedFashion","TCMFForecaster"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","quantization","customized_model"];
disCheck(disIds);
}
else if(vals.includes("customized_model")){
var ids = ["AutoTSEstimator","DeepARmodel","TFTmodel"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"];
disCheck(disIds);
}
}
else if(vals.length==2){
if(vals.includes("forecast") && vals.includes("hyperparameter_tuning")){
var ids = ["TuneaForecasting","AutoTSEstimator","AutoWIDE","AutoProphet","hyperparameter","taxiDataset","ONNX","AutoTSEstimator"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","quantization","distributed"];
disCheck(disIds);
}
else if(vals.includes("forecast") && vals.includes("anomaly_detection")){
var ids = ["AnomalyDetection"];
showTutorials(ids);
var disIds = ["simulation","hyperparameter_tuning","onnxruntime","quantization","distributed","customized_model"];
disCheck(disIds);
}
else if(vals.includes("forecast") && vals.includes("customized_model")){
var ids = ["DeepARmodel","TFTmodel","AutoTSEstimator"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"];
disCheck(disIds);
}
else if(vals.includes("forecast") && vals.includes("distributed")){
var ids = ["distributedFashion","TCMFForecaster"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","quantization","customized_model"];
disCheck(disIds);
}
else if(vals.includes("forecast") && vals.includes("quantization")){
var ids = ["Quantize"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","distributed","customized_model"];
disCheck(disIds);
}
else if(vals.includes("hyperparameter_tuning") && vals.includes("customized_model")){
var ids = ["AutoTSEstimator"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"];
disCheck(disIds);
}
else if(vals.includes("forecast") && vals.includes("onnxruntime")){
var ids = ["ONNX"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","quantization","distributed","customized_model"];
disCheck(disIds);
}
else if(vals.includes("hyperparameter_tuning") && vals.includes("onnxruntime")){
var ids = ["ONNX"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","quantization","distributed","customized_model"];
disCheck(disIds);
}
}
else if(vals.length==3){
if(vals.includes("forecast") && vals.includes("hyperparameter_tuning") && vals.includes("customized_model")){
var ids = ["AutoTSEstimator"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"];
disCheck(disIds);
}
else if(vals.includes("forecast") && vals.includes("hyperparameter_tuning") && vals.includes("onnxruntime")){
var ids = ["ONNX"];
showTutorials(ids);
var disIds = ["anomaly_detection","simulation","quantization","distributed","customized_model"];
disCheck(disIds);
}
}
});
//event when click the tags' buttons
$("details p button").click(function(){
var id = $(this).val();
$("#"+id).trigger("click");
});
// var allIds = ["forecast","anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","quantization","distributed","customized_model"];

324
docs/readthedocs/source/doc/Chronos/QuickStart/index.md
View file

@ -1,138 +1,240 @@
# Chronos Tutorial
- [**Predict Number of Taxi Passengers with Chronos Forecaster**](./chronos-tsdataset-forecaster-quickstart.html)
</br>
> ![](../../../../image/colab_logo_32px.png)[Run in Google Colab][chronos_nyc_taxi_tsdataset_forecaster_colab] &nbsp;![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][chronos_nyc_taxi_tsdataset_forecaster]
```eval_rst
.. raw:: html
In this guide we will demonstrate how to use _Chronos TSDataset_ and _Chronos Forecaster_ for time series processing and predict number of taxi passengers.
<link rel="stylesheet" type="text/css" href="../../../_static/css/chronos_tutorial.css" />
---------------------------
<div id="tutorial">
<h3 style="text-align:left">Filter:</h3>
<p>Please <span style="font-weight:bold;">check</span> the checkboxes or <span style="font-weight:bold;">click</span> 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. </p>
<div class="border">
<div class="choiceline">
<div class="choicebox"><input type="checkbox" class="checkboxes" name="choice" value="forecast" id="forecast">forecast </div>
<div class="choicebox"><input type="checkbox" class="checkboxes" name="choice" value="anomaly_detection" id="anomaly_detection">anomaly detection</div>
<div class="choicebox"><input type="checkbox" class="checkboxes" name="choice" value="simulation" id="simulation">simulation</div>
<div class="choicebox"><input type="checkbox" class="checkboxes" name="choice" value="hyperparameter_tuning" id="hyperparameter_tuning">AutoML</div>
</div>
<div class="choiceline">
<div class="choicebox"><input type="checkbox" class="checkboxes" name="choice" value="onnxruntime" id="onnxruntime">onnxruntime </div>
<div class="choicebox"><input type="checkbox" class="checkboxes" name="choice" value="quantization" id="quantization">quantization</div>
<div class="choicebox"><input type="checkbox" class="checkboxes" name="choice" value="distributed" id="distributed">distributed</div>
<div class="choicebox"><input type="checkbox" class="checkboxes" name="choice" value="customized_model" id="customized_model">customized model</div>
</div>
</div>
</br>
- [**Tune a Forecasting Task Automatically**](./chronos-autotsest-quickstart.html)
<details id="ChronosForecaster">
<summary>
<a href="./chronos-tsdataset-forecaster-quickstart.html">Predict Number of Taxi Passengers with Chronos Forecaster</a>
<p>Tag: <button value="forecast">forecast</button></p>
</summary>
<img src="../../../_images/colab_logo_32px.png"><a href="https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb">Run in Google Colab</a>
&nbsp;
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb">View source on GitHub</a>
<p>In this guide we will demonstrate how to use <span>Chronos TSDataset</span> and <span>Chronos Forecaster</span> for time series processing and predict number of taxi passengers.</p>
</details>
<hr>
> ![](../../../../image/colab_logo_32px.png)[Run in Google Colab][chronos_autots_nyc_taxi_colab] &nbsp;![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][chronos_autots_nyc_taxi]
<details id="TuneaForecasting">
<summary>
<a href="./chronos-autotsest-quickstart.html">Tune a Forecasting Task Automatically</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="hyperparameter_tuning">AutoML</button></p>
</summary>
<img src="../../../_images/colab_logo_32px.png"><a href="https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb">Run in Google Colab</a>
&nbsp;
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb">View source on GitHub</a>
<p>In this guide we will demonstrate how to use <span>Chronos AutoTSEstimator</span> and <span>Chronos TSPipeline</span> to auto tune a time seires forecasting task and handle the whole model development process easily.</p>
</details>
<hr>
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.
<details id="DetectAnomaly">
<summary>
<a href="./chronos-anomaly-detector.html">Detect Anomaly Point in Real Time Traffic Data</a>
<p>Tag: <button value="anomaly_detection">anomaly detection</button></p>
</summary>
<img src="../../../_images/colab_logo_32px.png"><a href="https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb">Run in Google Colab</a>
&nbsp;
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb">View source on GitHub</a>
<p>In this guide we will demonstrate how to use <span>Chronos Anomaly Detector</span> for real time traffic data from the Twin Cities Metro area in Minnesota anomaly detection.</p>
</details>
<hr>
---------------------------
<details id="AutoTSEstimator">
<summary>
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb">Tune a Customized Time Series Forecasting Model with AutoTSEstimator.</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="hyperparameter_tuning">AutoML</button>&nbsp;<button value="customized_model">customized model</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb">View source on GitHub</a>
<p>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 <span>AutoTSEstimator</span> to adjust the parameters of a customized model.</p>
</details>
<hr>
- [**Detect Anomaly Point in Real Time Traffic Data**](./chronos-anomaly-detector.html)
<details id="AutoWIDE">
<summary>
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb">Auto Tune the Prediction of Network Traffic at the Transit Link of WIDE</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="hyperparameter_tuning">AutoML</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb">View source on GitHub</a>
<p>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 <span>AutoTS</span> in project <span><a href="https://github.com/intel-analytics/bigdl/tree/main/python/chronos/src/bigdl/chronos">Chronos</a></span> to do time series forecasting in an automated and distributed way.</p>
</details>
<hr>
> ![](../../../../image/colab_logo_32px.png)[Run in Google Colab][chronos_minn_traffic_anomaly_detector_colab] &nbsp;![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][chronos_minn_traffic_anomaly_detector]
<details id="MultvarWIDE">
<summary>
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb">Multivariate Forecasting of Network Traffic at the Transit Link of WIDE</a>
<p>Tag: <button value="forecast">forecast</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb">View source on GitHub</a>
<p>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 <span><a href="https://github.com/intel-analytics/bigdl/tree/main/python/chronos/src/bigdl/chronos">Chronos</a></span>.</p>
</details>
<hr>
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.
<details id="MultstepWIDE">
<summary>
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb">Multistep Forecasting of Network Traffic at the Transit Link of WIDE</a>
<p>Tag: <button value="forecast">forecast</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb">View source on GitHub</a>
<p>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 <span><a href="https://github.com/intel-analytics/bigdl/tree/main/python/chronos/src/bigdl/chronos">Chronos</a></span>.</p>
</details>
<hr>
---------------------------
<details id="LSTMForecaster">
<summary>
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction.ipynb">Stock Price Prediction with LSTMForecaster</a>
<p>Tag: <button value="forecast">forecast</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction.ipynb">View source on GitHub</a>
<p>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.</p>
<p>Reference: <span><a href="https://github.com/jwkanggist/tf-keras-stock-pred">https://github.com/jwkanggist/tf-keras-stock-pred</a></span></p>
</details>
<hr>
- [**Tune a Customized Time Series Forecasting Model with AutoTSEstimator.**][network_traffic_autots_customized_model]
<details id="AutoProphet">
<summary>
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb">Stock Price Prediction with ProphetForecaster and AutoProphet</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="hyperparameter_tuning">AutoML</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb">View source on GitHub</a>
<p>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 <span><a href="https://www.kaggle.com/camnugent/sandp500/">data source</a></span>.</p>
<p>Reference: <span><a href="https://facebook.github.io/prophet">https://facebook.github.io/prophet</a></span>, <span><a href="https://github.com/jwkanggist/tf-keras-stock-pred">https://github.com/jwkanggist/tf-keras-stock-pred</a></span></p>
</details>
<hr>
> ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][network_traffic_autots_customized_model]
<details id="Unsupervised">
<summary>
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb">Unsupervised Anomaly Detection for CPU Usage</a>
<p>Tag: <button value="anomaly_detection">anomaly detection</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb">View source on GitHub</a>
<p>We demonstrates how to perform anomaly detection based on Chronos's built-in <span><a href="../../PythonAPI/Chronos/anomaly_detectors.html#dbscandetector">DBScanDetector</a></span>, <span><a href="../../PythonAPI/Chronos/anomaly_detectors.html#aedetector">AEDetector</a></span> and <span><a href="../../PythonAPI/Chronos/anomaly_detectors.html#thresholddetector">ThresholdDetector</a></span>.</p>
</details>
<hr>
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.
<details id="AnomalyDetection">
<summary>
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb">Anomaly Detection for CPU Usage Based on Forecasters</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="anomaly_detection">anomaly detection</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb">View source on GitHub</a>
<p>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 <span><a href="../../PythonAPI/Chronos/anomaly_detectors.html#thresholddetector">ThresholdDetector</a></span>.</p>
</details>
<hr>
---------------------------
<details id="DeepARmodel">
<summary>
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/DeepAR">Help pytorch-forecasting improve the training speed of DeepAR model</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="customized_model">customized model</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/DeepAR">View source on GitHub</a>
<p>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.</p>
</details>
<hr>
- [**Auto Tune the Prediction of Network Traffic at the Transit Link of WIDE**][network_traffic_autots_forecasting]
<details id="TFTmodel">
<summary>
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/TFT">Help pytorch-forecasting improve the training speed of TFT model</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="customized_model">customized model</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/TFT">View source on GitHub</a>
<p>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.</p>
</details>
<hr>
> ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][network_traffic_autots_forecasting]
<details id="hyperparameter">
<summary>
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/hpo/muti_objective_hpo_with_builtin_latency_tutorial.ipynb">Tune a Time Series Forecasting Model with multi-objective hyperparameter optimization.</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="hyperparameter_tuning">AutoML</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/hpo/muti_objective_hpo_with_builtin_latency_tutorial.ipynb">View source on GitHub</a>
<p>In this notebook, we demostrate how to use <span>multi-objective hyperparameter optimization with built-in latency metric</span> in project <span><a href="https://github.com/intel-analytics/bigdl/tree/main/python/chronos/src/bigdl/chronos">Chronos</a></span> to do time series forecasting and achieve good tradeoff between performance and latency.</p>
</details>
<hr>
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.
<details id="taxiDataset">
<summary>
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/auto_model">Auto tuning prophet on nyc taxi dataset</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="hyperparameter_tuning">AutoML</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/auto_model">View source on GitHub</a>
<p>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.</p>
</details>
<hr>
---------------------------
<details id="distributedFashion">
<summary>
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/distributed">Use Chronos forecasters in a distributed fashion</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="distributed">distributed</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/distributed">View source on GitHub</a>
<p>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.</p>
</details>
<hr>
- [**Multivariate Forecasting of Network Traffic at the Transit Link of WIDE**][network_traffic_model_forecasting]
<details id="ONNX">
<summary>
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/onnx">Use ONNXRuntime to speed-up forecasters' inferecing</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="onnxruntime">onnxruntime</button>&nbsp;<button value="hyperparameter_tuning">AutoML</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/onnx">View source on GitHub</a>
<p>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.</p>
</details>
<hr>
> ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][network_traffic_model_forecasting]
<details id="Quantize">
<summary>
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/quantization">Quantize Chronos forecasters method to speed-up inference</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="quantization">quantization</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/quantization">View source on GitHub</a>
<p>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.</p>
</details>
<hr>
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].
<details id="SimualateTimeSeriesData">
<summary>
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/simulator">Simualate time series data with similar pattern as example data</a>
<p>Tag: <button value="simulation">simulation</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/simulator">View source on GitHub</a>
<p>This example shows how to generate synthetic data with similar distribution as training data with the fast and easy DPGANSimulator API provided by Chronos.</p>
</details>
<hr>
---------------------------
<details id="TCMFForecaster">
<summary>
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/tcmf">High dimension time series forecasting with Chronos TCMFForecaster</a>
<p>Tag: <button value="forecast">forecast</button>&nbsp;<button value="distributed">distributed</button></p>
</summary>
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/tcmf">View source on GitHub</a>
<p>This example demonstrates how to use BigDL Chronos TCMFForecaster to run distributed training and inference for high dimension time series forecasting task.</p>
</details>
<hr>
- [**Multistep Forecasting of Network Traffic at the Transit Link of WIDE**][network_traffic_multivariate_multistep_tcnforecaster]
</div>
> ![](../../../../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: *<https://github.com/jwkanggist/tf-keras-stock-pred>*
---------------------------
- [**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: *<https://facebook.github.io/prophet>*, *<https://github.com/jwkanggist/tf-keras-stock-pred>*
---------------------------
- [**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.
---------------------------
- [**Tune a Time Series Forecasting Model with multi-objective hyperparameter optimization.**][pytorch_forecasting_mo_tune]
> ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][pytorch_forecasting_mo_tune]
In this notebook, we demostrate how to use _multi-objective hyperparameter optimization with built-in latency metric_ in project [Chronos][chronos] to do time series forecasting and achieve good tradeoff between performance and latency.
[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>
[pytorch_forecasting_deepar]: <https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/DeepAR>
[pytorch_forecasting_tft]: <https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/TFT>
[pytorch_forecasting_mo_tune]: <https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/hpo/muti_objective_hpo_with_builtin_latency_tutorial.ipynb>
<script src="../../../_static/js/chronos_tutorial.js"></script>
```