Time Series Summary - Algorithm by TimeSeries

No algorithm description given

Time Series Summary Returns various statistics of the given time series. Input Formats the algorithm has 2 input formats, a Json input & a standard array input. The output values are the same for all. Json Input Uniformly Spaced Input 
{
 "uniformData": Double[]
}
 
{
 "uniformData": [0,1,2,3,4,5]
}
 input – input is a 1D array filled with uniformly spaced y coordinates (IE: input[y] where dx(1) = dx(n) ) Variably Spaced Input 
{
 "dynamicData": Double[][]
}
 
{
 "dynamicData": [[1,2,3,4,5],[1,2,3,4,5]]
}
 input – input is a 2D array where with the first axis X and second Y (IE: input[X][Y] ) Standard Array Input Uniformly Spaced Input input: Double[] [1,2,3,4,5,6,7,8,9,10] input – input is a 1D array filled with uniformly spaced y coordinates (IE: input[y] where dx(1) = dx(n) ) Variably Spaced Input [
 X: Double[], 
 y: Double[]
] 
 [[1,2,3,4,5,6,7,8,9,10],[2,4,6,8,10,12,14]] x – X axis of the resultant 2D dataset. y - Y axis of the resultant 2D dataset. Output Format 
{
 "max": Double,
 "var": Double,
 "geometricMean": Double,
 "populationVariance":Double,
 "slope": Double,
 "kurtosis": Double,
 "min": Double,
 "correlation": Double,
 "intercept": Double,
 "mean": Double,
 "rmse": Double,
 "skewness": Double,
 "standardDeviation": Double
}
 max – The maximum value of the dataset. min – The minimum value of the dataset. geometricMean – The Geometric Mean of the dataset. populationVariance – The Population Variance of the dataset slope – The slope (y2-y1)/(x2-x1) of the dataset. kurtosis – The Kurtosis of the dataset. correlation – the Correlation between X & Y for the dataset. intercept – The y intercept for the dataset. mean – The average Y value for the dataset. rmse – The Root Mean Square Deviation of the dataset. skewness – The Skewness of the dataset. standardDeviation - The Standard Deviation of the dataset.

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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/TimeSeriesSummary/0.1.2

View cURL Docs

algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://TimeSeries/TimeSeriesSummary/0.1.2 -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/TimeSeriesSummary/0.1.2")
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/TimeSeriesSummary/0.1.2");
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/TimeSeriesSummary/0.1.2")
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/TimeSeriesSummary/0.1.2")
           .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/TimeSeriesSummary/0.1.2");
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/TimeSeriesSummary/0.1.2")
           .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/TimeSeriesSummary/0.1.2')
print algo.pipe(input)

View Python Docs

library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("TimeSeries/TimeSeriesSummary/0.1.2")
result <- algo$pipe(input)$result
print(result)

View R Docs

require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('TimeSeries/TimeSeriesSummary/0.1.2')
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/TimeSeriesSummary/0.1.2");
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/TimeSeriesSummary/0.1.2") { resp, error in
  print(resp)
}

View Swift Docs

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