Time Series Summary

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,
 "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...

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/TimeSeriesSummary/0.1.2
View cURL Docs
algo auth
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)
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);
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)
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/TimeSeriesSummary/0.1.2");
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/TimeSeriesSummary/0.1.2')
print algo.pipe(input)
View Python Docs

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