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In this example we want to predict TD Principal of the [Apache Kafka](https://github.com/apache/kafka) test project for 10 steps ahead. Apache Kafka dataset contains 150 versions, so we expect the response to contain predicted TD values for versions 151 to 160. We set the *horizon* parameter to *10* and the *regressor* parameter to *auto*, in order to allow the TD Forecaster to choose the best model based on training error minimization.
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Once the server is running, open your web browser and navigate to the following URL:
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http://127.0.0.1:5000/ForecasterToolbox/TDForecasting?horizon=10&project=apache_kafka_measures®ressor=auto&ground_truth=yes&test=no
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The response of the submitted command is a JSON object containing the forecasted TD values of the Apache Kafka test project for versions 151 to 160 (10 steps) ahead, using the Ridge regressor, which the back-end selected as the most appropriate model for this occasion. This JSON object is illustrated below:
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```json
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{
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"message": "The request was fulfilled.",
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"results": {
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"forecasts": [{
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"value": 62319.09731430385,
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"version": 151
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},
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{
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"value": 62516.381787162965,
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"version": 152
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},
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{
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"value": 62690.433655431836,
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"version": 153
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},
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{
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"value": 62870.72795431035,
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"version": 154
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},
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{
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"value": 63045.263349635316,
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"version": 155
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},
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{
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"value": 63226.55220201901,
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"version": 156
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},
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{
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"value": 63422.0804451476,
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"version": 157
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},
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{
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"value": 63630.16211082426,
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"version": 158
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},
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{
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"value": 63849.70018133768,
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"version": 159
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},
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{
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"value": 64080.05189562486,
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"version": 160
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}
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],
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"parameters": {
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"ground_truth": "no",
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"horizon": 10,
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"project": "apache_kafka_measures",
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"regressor": "ridge",
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"test": "no"
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}
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},
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"status": 200
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}
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``` |
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\ No newline at end of file |