Threshold Anomaly Detection - Algorithm by TimeSeries

No algorithm description given

Threshold Anomaly Detection This algorithm detects out of bounds data-points from a time-series dataset and sets them to 0. Table of Contents Introduction Inputs Outputs Algorithm Console Introduction By using the pre-defined upper & lower bounds, this algorithm will keep all data-points that lie within the thresholds. All data-points that lie outside are set to 0. Inputs This algorithm has two input formats; JSON Formatting and Array Formatting. JSON Format { 
 "data": Double[],
 "lowerBound": Double,
 "upperBound": Double
} { 
 "data": [1,2,3,4,3,4,6,7,5,4,2,1,1],
 "lowerBound": 1.4,
 "upperBound": 6.5
} data - (required) - The time series input data you wish to detect anomalies on, this algorithm assumes the values are evenly spaced. ( dx(1) == dx(n) ) lowerBound - (optional) - The lower boundary limit, the default value is 0. upperBound - (optional) - The upper boundary limit, the default value is 0. Array Format [
 data: Double[],
 lowerBound: Double,
 upperBound: Double
] [[1,2,3,4,3,4,6,7,5,4,2,1,1], 1.4, 6.5] data - (required) - The time-series input data you wish to detect anomalies on, this algorithm assumes the values are evenly spaced. ( dx(1) == dx(n) ) lowerBound - (required) - The lower boundary limit. upperBound - (required) - The upper boundary limit. Outputs This algorithm has json output for json input, and array output for array input. JSON Format { 
 "output": Double[]
} { 
 "output": [0,2,3,4,3,4,6,0,5,4,2,0,0]
} output - The filtered dataset, if a data-point was considered out of bounds it’s value is set to 0. Array Format output: Double[] [0,2,3,4,3,4,6,0,5,4,2,0,0] output - The filtered dataset, if a datapoint was considered out of bounds it’s value is set to 0.

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1. Type your input

2. See the result

Running algorithm...

curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY' https://api.algorithmia.com/v1/algo/TimeSeries/ThresholdAnomalyDetection/0.2.0

View cURL Docs

algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://TimeSeries/ThresholdAnomalyDetection/0.2.0 -d '{{input | formatInput:"cli"}}'

View CLI Docs

import (
  algorithmia "github.com/algorithmiaio/algorithmia-go"
)

input := {{input | formatInput:"go"}}

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://TimeSeries/ThresholdAnomalyDetection/0.2.0")
resp, _ := algo.Pipe(input)
response := resp.(*algorithmia.AlgoResponse)
fmt.Println(response.Result)

View Go Docs

import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://TimeSeries/ThresholdAnomalyDetection/0.2.0");
AlgoResponse result = algo.pipeJson(input);
System.out.println(result.asJsonString());

View Java Docs

import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://TimeSeries/ThresholdAnomalyDetection/0.2.0")
val result = algo.pipeJson(input)
System.out.println(result.asJsonString)

View Scala Docs

var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://TimeSeries/ThresholdAnomalyDetection/0.2.0")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });

View Javascript Docs

var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://TimeSeries/ThresholdAnomalyDetection/0.2.0")
           .pipe(input)
           .then(function(response) {
             console.log(response.get());
           });

View NodeJS Docs

import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('TimeSeries/ThresholdAnomalyDetection/0.2.0')
print algo.pipe(input)

View Python Docs

library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("TimeSeries/ThresholdAnomalyDetection/0.2.0")
result <- algo$pipe(input)$result
print(result)

View R Docs

require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('TimeSeries/ThresholdAnomalyDetection/0.2.0')
puts algo.pipe(input).result

View Ruby Docs

use algorithmia::*;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("TimeSeries/ThresholdAnomalyDetection/0.2.0");
let response = algo.pipe(input);

View Rust Docs

import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "TimeSeries/ThresholdAnomalyDetection/0.2.0") { resp, error in
  print(resp)
}

View Swift Docs

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