No algorithm description given
Forecast Gives a forecast the next n steps of a given time series based on extrapolation of linear and periodic trends. Table of Contents Description Inputs Outputs Algorithm Console Description This Forecasting algorithm works by fitting a linear trend to the given data, extrapolating it into the future series interval, and then adjusting it based on the expected contributions of each detected periodic component. Periodicity is detected using AutoCorrelate . Note that this algorithm only deals with linear and periodic trends. If other artifacts, such as non-periodic spikes or super/sub-linear trends, are present in the data, results will suffer, and as a general rule the further you go in the future, the less accuracy you should expect. Inputs This algorithm has both a JSON Object Input and a Array Input format. JSON Format This algorithm has a single JSON input format. { "series": Double[], "steps": Int, "maxNumPeriods": Int } { "series": [1,2,3,4,3,4.62,6,7,5,4,2.01,1,1], "stepsToPredict": 3, "maxNumPeriods": 2 } series - (required) - the input time series dataset. this algorithm assumes the values are evenly spaced. ( dx(1) == dx(n) ) stepsToPredict - (optional) - the number of timesteps to predict, defaults to the length of the series dataset. The further into the future you predict, the worse the accuracy becomes, so it makes sense to set a value other than the default. maxNumPeriods - (optional) - the number of periods to consider, strongest first, defaults to a value of 1. Array Format There are two array input formats; basic and advanced. Basic Format series: Double[] [1,2,3,4,3,4,6,7,5,4,2,1,1] series - (required) - the input time series dataset. this algorithm assumes the values are evenly spaced. ( dx(1) == dx(n) ) Advanced Format [ series: Double[], stepsToPredict: Int, maxNumOfPeriods: Int } [[1,2,3,4,3,4,6,7,5,4,2,1,1], 4, 2] series - (required) - the input time series dataset. this algorithm assumes the values are evenly spaced. ( dx(1) == dx(n) ) stepsToPredict - (optional) - the number of timesteps to predict, defaults to the length of the series dataset. The further into the future you predict, the worse the accuracy becomes, so it makes sense to set a value other than the default. maxNumPeriods - (optional) - the number of periods to consider, strongest first, defaults to a value of 1. Outputs If a JSON object as passed as an input, the JSON format is returned, otherwise the Array format is returned. JSON Format { "predicted": Double[] } { "predicted": [11, 12, 13, 14, 15] } predicted - the predicted datapoints extending from the last datapoint in series . Array Format predicted: Double[] [1,2,3,4,3,4,6,7,5,4,2,1,1] predicted - the predicted datapoints extending from the last datapoint in series .
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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/Forecast/0.2.0
algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://TimeSeries/Forecast/0.2.0 -d '{{input | formatInput:"cli"}}'
import (
algorithmia "github.com/algorithmiaio/algorithmia-go"
)
input := {{input | formatInput:"go"}}
var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://TimeSeries/Forecast/0.2.0")
resp, _ := algo.Pipe(input)
response := resp.(*algorithmia.AlgoResponse)
fmt.Println(response.Result)
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/Forecast/0.2.0");
AlgoResponse result = algo.pipeJson(input);
System.out.println(result.asJsonString());
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/Forecast/0.2.0")
val result = algo.pipeJson(input)
System.out.println(result.asJsonString)
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
.algo("algo://TimeSeries/Forecast/0.2.0")
.pipe(input)
.then(function(output) {
console.log(output);
});
using Algorithmia;
var input = "{{input | formatInput:"cs"}}";
var client = new Client("YOUR_API_KEY");
var algorithm = client.algo("algo://TimeSeries/Forecast/0.2.0");
var response = algorithm.pipe<object>(input);
Console.WriteLine(response.result);
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
.algo("algo://TimeSeries/Forecast/0.2.0")
.pipe(input)
.then(function(response) {
console.log(response.get());
});
import Algorithmia
input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('TimeSeries/Forecast/0.2.0')
print algo.pipe(input)
library(algorithmia)
input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("TimeSeries/Forecast/0.2.0")
result <- algo$pipe(input)$result
print(result)
require 'algorithmia'
input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('TimeSeries/Forecast/0.2.0')
puts algo.pipe(input).result
use algorithmia::Algorithmia;
let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("TimeSeries/Forecast/0.2.0");
let response = algo.pipe(input);
import Algorithmia
let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "TimeSeries/Forecast/0.2.0") { resp, error in
print(resp)
}
