Outlier Detection - Algorithm by TimeSeries

No algorithm description given

Outlier Detection Detects outliers in time series data. Contains two input formats & 1 output format. Input Formats Automatic input: Double[] [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 9 , 25 , 4 , 3 ] input – a Time series dataset with evenly spaced datapoints. An outlier is defined by any datapoint outside of 2 standard deviations from the mean with this input. Manual [
 input: Double[],
 t: Double
] [[1,2,3,4,5,6,7,9,25,4,3, 2.0] input – a Time series dataset with evenly spaced datapoints. t – defines the maximum number of standard deviations from the mean with which a datapoint is not considered an outlier. Output Formats output: Double[] [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 25 , 0 , 0 ] output – is identical in size to the input dataset. If a datapoint is not considered an outlier then it’s indicies’ value is set to 0. If a datapoint is an outlier, it’s indicies’ value is kept from the input.

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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/OutlierDetection/0.1.0

View cURL Docs

algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://TimeSeries/OutlierDetection/0.1.0 -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/OutlierDetection/0.1.0")
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/OutlierDetection/0.1.0");
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/OutlierDetection/0.1.0")
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/OutlierDetection/0.1.0")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });

View Javascript Docs

using Algorithmia;

var input = "{{input | formatInput:"cs"}}";
var client = new Client("YOUR_API_KEY");
var algorithm = client.algo("algo://TimeSeries/OutlierDetection/0.1.0");
var response = algorithm.pipe<object>(input);
Console.WriteLine(response.result);

View .NET/C# Docs

var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://TimeSeries/OutlierDetection/0.1.0")
           .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/OutlierDetection/0.1.0')
print algo.pipe(input)

View Python Docs

library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("TimeSeries/OutlierDetection/0.1.0")
result <- algo$pipe(input)$result
print(result)

View R Docs

require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('TimeSeries/OutlierDetection/0.1.0')
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/OutlierDetection/0.1.0");
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/OutlierDetection/0.1.0") { resp, error in
  print(resp)
}

View Swift Docs

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