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,
 "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.

Tags
(no tags)

Cost Breakdown

0 cr
royalty per call
1 cr
usage per second
avg duration

Cost Calculator

API call duration (sec)
×
API calls
=
Estimated cost
per calls
for large volume discounts
For additional details on how pricing works, see Algorithmia pricing.

No permissions required

This algorithm does not require any special permissions.


To understand more about how algorithm permissions work, see the permissions documentation.

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
# Enter API Key: YOUR_API_KEY
algo run algo://TimeSeries/TimeSeriesSummary/0.1.2 -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/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
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::*;

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
Discussion
  • {{comment.username}}