##5.2. Time Series Statistics Tool
The result of mean, geometricMean, quadraticMean, median, mode, min, max, sum, count, variance, populationVariance, standardDeviation, Logarithm, LogarithmDifference, Ratio, Logit, MoveAverage, MoveMedian, WhiteNoise, n-Order Differences, autoCovariance, autoCorrelation, PartialAutoCovariance, PartialAutoCorrelation will be returned.
https://acrolearner.p.rapidapi.com/v0_1/CltService/tools/tsstat
http method: post

Request Header parameters:

Content-Type: application/json
x-rapidapi-host: acrolearner.p.rapidapi.com
x-rapidapi-key: [your rapidapi-key]

Request Query parameters Example:

moveInterval=2&convertMode=0&smoothMode=0&diffOrder=3
moveInterval: Calculating period for MovingAverage or MovingMedian
convertMode: Convert Mode(0:original,1:Diff,2:Log,3:LogDiff,4:Ratio,5:Logit)
smoothMode: Smooth Mode(0:no,1:MoveAverage,2:MoveMedian)
diffOrder: Difference floors for Calculation

Request JSON Example:

{
  "trainText": "1.4 2.3 3.6 4.1"
}

Response JSON Example:

{
    "contents": {
        "autoCorrelation": -0.8339677496795179,
        "autoCovariance": -0.8875000000000001,
        "count": 4,
        "differences": [
            "0.8999999999999999 1.3000000000000003 0.49999999999999956",
            "0.40000000000000036 -0.8000000000000007",
            "-1.200000000000001"
        ],
        "geometricMean": 2.6256422812396676,
        "logarithm": "0.3364722366212129 0.8329091229351039 1.2809338454620642 1.410986973710262",
        "logDiff": "0.496436886313891 0.44802472252696035 0.1300531282481978",
        "logit": null,
        "max": 4.1,
        "mean": 2.8499999999999996,
        "median": 2.95,
        "min": 1.4,
        "mode": 1.4,
        "moveAverage": "1.8499999999999999 2.95",
        "moveMedian": "1.8499999999999999 2.95",
        "partialAutoCorrelation": 0,
        "partialAutoCovariance": 0,
        "popVariance": 1.1324999999999998,
        "quadraticMean": 3.042203149035251,
        "ratio": "1.6428571428571428 1.565217391304348 1.1388888888888888",
        "stdDeviation": 1.064189832689638,
        "sum": 11.399999999999999,
        "variance": 1.5099999999999998,
        "whiteNoise": "2.42897010204453 4.0694376074286165 4.076824606554626 3.6095171447293266"
    },
    "errors": null,
    "memo": "Congratulations on your success!",
    "result": true
}

“mean”: mean value from number array of “trainText”
“geometricMean”: geometricMean value from number array of “trainText”
“quadraticMean”: quadraticMean value from number array of “trainText”
“median”: median value from number array of “trainText”
“mode”: mode value from number array of “trainText”
“min”: min value from number array of “trainText”
“max”: max value from number array of “trainText”
“sum”: sum value from number array of “trainText”
“count”: count value from number array of “trainText”
“variance”: variance value from number array of “trainText”
“popVariance”: populationVariance value from number array of “trainText”
“stdDeviation”: standardDeviation value from number array of “trainText”, calculate by populationVariance
"logarithm": logarithm value from number array of “trainText”
“logDiff”: LogarithmDifference value from number array of “trainText”
“ratio”: ratio value from number array of “trainText”
“logit”: logit value from number array of “trainText”
“moveAverage”: moveAverage value from number array of “trainText” and parameter “moveInterval”
“moveMedian”: moveMedian value from number array of “trainText” and parameter “moveInterval”
“whiteNoise”: whiteNoise value from number array of “trainText”
“differences”: differences array from number array of “trainText” and parameter “diffOrder”
“autoCovariance”: autoCovariance value from number array of “trainText” and parameter “convertMode”&“smoothMode”
“autoCorrelation”: autoCorrelation value from number array of “trainText” and parameter “convertMode”&“smoothMode”
“partialAutoCovariance”: partialAutoCovariance value from number array of “trainText” and parameter “convertMode”&“smoothMode”
“partialAutoCorrelation”: partialAutoCorrelation value from number array of “trainText” and parameter “convertMode”&“smoothMode”