Threshold Anomaly Detection

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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

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3. Use this algorithm

curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY'
View cURL Docs
algo auth
algo run algo://TimeSeries/ThresholdAnomalyDetection/0.2.0 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import (
  algorithmia ""

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)
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);
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)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(output) {
View Javascript Docs
using Algorithmia;

var input = "{{input | formatInput:"cs"}}";
var client = new Client("YOUR_API_KEY");
var algorithm = client.algo("algo://TimeSeries/ThresholdAnomalyDetection/0.2.0");
var response = algorithm.pipe<object>(input);
View .NET/C# Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(response) {
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

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