Autocorrelate

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Autocorrelate Autocorrelate   finds the dataset’s cross-correlation with itself to discover periodic components while filtering out non-periodic noise. Table of Contents Inputs Outputs Algorithm Console Inputs This algorithm has a JSON object input, as well as array input formatting. JSON Format 
{ 
 "data":Double[]
}
 
{ 
 "data": [1,2,3,4,3,4,6,7,5,4,2,1,1]
}
 data -   (required)   - The timeseries input data you wish to Autocorrelate, this algorithm assumes the values are evenly spaced. ( dx(1) == dx(n) ) Array Format There is a single array input format for this algorithm. Basic format data: Double[] [1,2,3,4,3,4,6,7,5,4,2,1,1] data -   (required)   - The timeseries input data you wish to Autocorrelate, this algorithm assumes the values are evenly spaced. ( dx(1) == dx(n) ) Outputs This algorithm has json output for json input, and array output for array input. JSON Format 
{ 
 "autoCorrelated": Double[]
}
 
{ 
 "autoCorrelated": [4,3,2]
}
 autoCorrelated - The autocorrelated result from the partially periodic input data. Array Format autoCorrelated: Double[] [1,2,3,4,3,4,6,7,5,4,2,1,1] autoCorrelated - The autocorrelated result from the partially periodic input data.

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1. Type your input

2. See the result

Running algorithm...

3. Use this 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/Autocorrelate/0.2.0
View cURL Docs
algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://TimeSeries/Autocorrelate/0.2.0 -d '{{input | formatInput:"cli"}}'
View CLI 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/Autocorrelate/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/Autocorrelate/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/Autocorrelate/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/Autocorrelate/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/Autocorrelate/0.2.0')
print algo.pipe(input)
View Python Docs
library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("TimeSeries/Autocorrelate/0.2.0")
result <- algo$pipe(input)$result
print(result)
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('TimeSeries/Autocorrelate/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/Autocorrelate/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/Autocorrelate/0.2.0") { resp, error in
  print(resp)
}
View Swift Docs
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