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PHP and Machine Learning: How to Perform Anomaly Detection on Time Series Data

M66 2025-07-13

Introduction

In today's data-driven world, handling and analyzing time series data has become essential. Time series data is arranged chronologically and contains a sequence of observations or measurements. Anomaly detection in time series data is a crucial task, helping businesses and organizations identify abnormal behavior and take necessary actions. This article will introduce how to use PHP and machine learning techniques for anomaly detection on time series data.

Preparing the Data

First, we need to prepare the time series data. Suppose we have a dataset that records daily sales, and we want to use these sales figures for anomaly detection. Here is an example dataset:

$dateSales = [
    ['2019-01-01', 100],
    ['2019-01-02', 120],
    ['2019-01-03', 80],
    ['2019-01-04', 90],
    ['2019-01-05', 110],
    // More data for other dates...
];

Data Preprocessing

Before performing anomaly detection, we need to preprocess the data. First, we convert the dates into timestamps to make it easier for machine learning algorithms to process. Then, we normalize the sales data to scale it into a smaller range, preventing large differences in feature values from affecting anomaly detection. Here’s the code to preprocess the data:

// Convert dates to timestamps
foreach ($dateSales as &$data) {
    $data[0] = strtotime($data[0]);
}

// Normalize the sales data
$sales = array_column($dateSales, 1);
$scaledSales = [];
$minSales = min($sales);
$maxSales = max($sales);
foreach ($sales as $sale) {
    $scaledSales[] = ($sale - $minSales) / ($maxSales - $minSales);
}

Selecting the Anomaly Detection Algorithm

Before performing anomaly detection, we need to choose the right machine learning algorithm. Common algorithms for time series anomaly detection include statistical methods, clustering methods, and deep learning approaches. In this article, we will use the ARIMA (AutoRegressive Integrated Moving Average) algorithm for anomaly detection.

Using the ARIMA Algorithm for Anomaly Detection

ARIMA is a widely used statistical model for time series analysis. In PHP, we can use the `arima` function from the stats library to implement ARIMA for anomaly detection. Below is an example of how to use ARIMA for anomaly detection:

$data = new StatsTimeSeries($scaledSales);

// Fit the model
$arima = StatsARIMA::fit($data);

// Predict the next data point
$prediction = $arima->predict();

// Calculate the residual error
$residual = $data->last() - $prediction;

// Set the threshold for anomaly detection
$errorThreshold = 0.05;

if (abs($residual) > $errorThreshold) {
    echo "Anomaly detected!";
} else {
    echo "No anomaly detected.";
}

In the code above, we first use the TimeSeries and ARIMA classes from the stats library to initialize and fit the model. Then, we predict the next data point and calculate the residual error. Finally, we set a threshold to check whether the residual error exceeds the normal range, which indicates the presence of an anomaly.

Conclusion

This article demonstrated how to use PHP and machine learning techniques for anomaly detection on time series data. We first prepared and preprocessed the time series data, then selected the ARIMA algorithm and implemented it using PHP’s stats library. By performing residual error thresholding, we can effectively identify anomalies. We hope this article helps readers understand and apply anomaly detection methods for time series data.

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