Linear Detrend - Algorithm by TimeSeries

No algorithm description given

Linear Detrend Takes a time series dataset, fits a linear trend to the data, and returns the dataset with the linear trend subtracted out. Table of Contents Inputs Outputs Algorithm Console Inputs JSON Format You must set either uniformlySpaced or variablySpaced json values, but not both. {
 "uniformlySpaced": Double[],
 "variablySpaced": Double[][]
}
 uniformly spaced {"uniformlySpaced": [1,2,3,4,3,4,6,7,5,4,2,1,1]}
 variably spaced {"variablySpaced": [[1,2,3,4,5,6,6.5,7,8.5,9,10,11,12],[1,2,3,4,3,4,6,7,5,4,2,1,1]]}
 uniformlySpaced - (pick one) - dataset is a 1D array filled with uniformly spaced y coordinates, assumes all datapoints are uniformly spaced in the X axis (IE: input[y] where dx(1) = dx(n) ) variablySpaced - (pick one) - Dataset is a 2D array where with the first axis X and second Y (IE: input[X][Y] ). both arrays must be the same size. Array Format [
 first: Double[],
 second: Double[]
]
 
 uniformly spaced [[1,2,3,4,3,4,6,7,5,4,2,1,1]]
 variably spaced [[1,2,4,8,16,32,64,128,256,512],[1,2,3,4,3,4,6,7,5,4]]
 first - (required) - if second is not defined, this field defines the dataset's Y axis, and assumes that all datapoints are uniformly spaced in the X axis (IE: dx(1) = dx(n) ) second - (optional) - if defined, first becomes the X axis coordinates for each datapoint, and second becomes the Y axis for each datapoint. Outputs This algorithm output's in the same format it recieved input: if you passed it JSON data it returns JSON data, if you passed it array data it returns array data, etc. JSON Format {"output": [
 -2.373626373626374,
 -1.3626373626373631,
 -0.3516483516483522,
 0.6593406593406588,
 -0.3296703296703303,
 0.6813186813186807,
 2.6923076923076916,
 3.7032967032967026,
 1.7142857142857135,
 0.7252747252747245,
 -1.2637362637362641,
 -2.252747252747253,
 -2.241758241758242
]}
 output - The input datset with any linear trend removed. Array Format [
 -2.373626373626374,
 -1.3626373626373631,
 -0.3516483516483522,
 0.6593406593406588,
 -0.3296703296703303,
 0.6813186813186807,
 2.6923076923076916,
 3.7032967032967026,
 1.7142857142857135,
 0.7252747252747245,
 -1.2637362637362641,
 -2.252747252747253,
 -2.241758241758242
]
 output - The input datset with any linear trend removed.

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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/LinearDetrend/0.1.1

View cURL Docs

algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://TimeSeries/LinearDetrend/0.1.1 -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/LinearDetrend/0.1.1")
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/LinearDetrend/0.1.1");
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/LinearDetrend/0.1.1")
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/LinearDetrend/0.1.1")
           .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/LinearDetrend/0.1.1");
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/LinearDetrend/0.1.1")
           .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/LinearDetrend/0.1.1')
print algo.pipe(input)

View Python Docs

library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("TimeSeries/LinearDetrend/0.1.1")
result <- algo$pipe(input)$result
print(result)

View R Docs

require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('TimeSeries/LinearDetrend/0.1.1')
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/LinearDetrend/0.1.1");
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/LinearDetrend/0.1.1") { resp, error in
  print(resp)
}

View Swift Docs

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