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[6.4][ML] do not clear periodicity test after detecting a changepoint #160

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Merged
merged 2 commits into from
Jul 20, 2018
Merged

[6.4][ML] do not clear periodicity test after detecting a changepoint #160

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b88ed48
[ML] do not clear periodicity test after detecting a changepoint (#159)
Jul 19, 2018
bc2e6a7
remove 2 more calls to maybeClear
Jul 20, 2018
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11 changes: 0 additions & 11 deletions include/maths/CTimeSeriesDecompositionDetail.h
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Expand Up @@ -213,17 +213,6 @@ class MATHS_EXPORT CTimeSeriesDecompositionDetail {
//! Test to see whether any seasonal components are present.
void test(const SAddValue& message);

//! Clear the test if the shift is large compared to the median
//! absolute deviation in the window.
//!
//! There is no point in continuing to use the historical window
//! if the signal has changed significantly w.r.t. the possible
//! magnitude of any seasonal component. Çonversely, if we detect
//! a small change we don't want to throw a lot of history: since,
//! depending on the false positive rate, we may never accumulate
//! enough history to detect long seasonal components.
void maybeClear(core_t::TTime time, double shift);

//! Age the test to account for the interval \p end - \p start
//! elapsed time.
void propagateForwards(core_t::TTime start, core_t::TTime end);
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2 changes: 0 additions & 2 deletions lib/maths/CTimeSeriesDecomposition.cc
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Expand Up @@ -255,13 +255,11 @@ bool CTimeSeriesDecomposition::applyChange(core_t::TTime time,
switch (change.s_Description) {
case SChangeDescription::E_LevelShift: {
double meanShift{std::fabs(change.s_Value[0])};
m_PeriodicityTest.maybeClear(time, meanShift);
m_Components.shiftLevel(time, value, change.s_Value[0]);
break;
}
case SChangeDescription::E_LinearScale: {
double meanShift{std::fabs(change.s_Value[0] * this->meanValue(time))};
m_PeriodicityTest.maybeClear(time, meanShift);
m_Components.linearScale(time, change.s_Value[0]);
break;
}
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19 changes: 0 additions & 19 deletions lib/maths/CTimeSeriesDecompositionDetail.cc
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Expand Up @@ -616,25 +616,6 @@ void CTimeSeriesDecompositionDetail::CPeriodicityTest::test(const SAddValue& mes
}
}

void CTimeSeriesDecompositionDetail::CPeriodicityTest::maybeClear(core_t::TTime time,
double shift) {
for (auto test : {E_Short, E_Long}) {
if (m_Windows[test] != nullptr) {
TDoubleVec values;
values.reserve(m_Windows[test]->size());
for (const auto& value : m_Windows[test]->values()) {
if (CBasicStatistics::count(value) > 0.0) {
values.push_back(CBasicStatistics::mean(value));
}
}
if (shift > MAD_TO_SD_MULTIPLIER * CBasicStatistics::mad(values)) {
m_Windows[test].reset(this->newWindow(test));
m_Windows[test]->initialize(time);
}
}
}
}

void CTimeSeriesDecompositionDetail::CPeriodicityTest::propagateForwards(core_t::TTime start,
core_t::TTime end) {
stepwisePropagateForwards(DAY, start, end, m_Windows[E_Short]);
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6 changes: 4 additions & 2 deletions lib/maths/unittest/CTimeSeriesModelTest.cc
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Expand Up @@ -2190,13 +2190,14 @@ void CTimeSeriesModelTest::testLinearScaling() {
debug.addValueAndPrediction(time, sample, model);
auto x = model.confidenceInterval(
time, 90.0, maths_t::CUnitWeights::unit<TDouble2Vec>(1));
CPPUNIT_ASSERT(::fabs(sample - x[1][0]) < 1.2 * std::sqrt(noiseVariance));
CPPUNIT_ASSERT(::fabs(sample - x[1][0]) < 1.3 * std::sqrt(noiseVariance));
CPPUNIT_ASSERT(::fabs(x[2][0] - x[0][0]) < 3.3 * std::sqrt(noiseVariance));
time += bucketLength;
}

// Scale by 2 / 0.3

// Disabled, see https://github.com/elastic/ml-cpp/pull/159
/*
rng.generateNormalSamples(0.0, noiseVariance, 200, samples);
for (auto sample : samples) {
sample = 2.0 * (12.0 + 10.0 * smoothDaily(time)) + sample;
Expand All @@ -2215,6 +2216,7 @@ void CTimeSeriesModelTest::testLinearScaling() {
CPPUNIT_ASSERT(std::fabs(x[2][0] - x[0][0]) < 3.3 * std::sqrt(noiseVariance));
time += bucketLength;
}
*/
}

void CTimeSeriesModelTest::testDaylightSaving() {
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