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Fixing double and triple exponential smoothing algorithms, and adding an accompanying test suite #2629

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cf03eb8
[MINOR] Exception handling for exponential smoothing algorithms
Sep 29, 2026
7ca015e
Improved exception handling, commenting, as well as error correction
Oct 20, 2026
8892bb3
Completion of correction and testing
Oct 31, 2026
14ca4ab
Merge pull request #1 from john-on-line27/feature/ema-utils-tests
john-on-line27 Oct 31, 2026
77b517f
Fixing the input values for R functions
Oct 31, 2026
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76 changes: 71 additions & 5 deletions src/main/java/org/apache/sysds/runtime/util/EMAUtils.java
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,7 @@ public double coef() {
public class EMAUtils {

public static FrameBlock exponentialMovingAverageImputation(FrameBlock block, int search_iterations, String mode,
int freq, Double alpha, Double beta, Double gamma) {
int freq, Double alpha, Double beta, Double gamma) {
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int cols = block.getNumColumns();
int rows = block.getNumRows();

Expand Down Expand Up @@ -145,7 +145,32 @@ public Container(Double[] vals, double error) {
double rsme;
}

public static void validateRange(Double... values) {
if (values == null || values.length == 0){
throw new IllegalArgumentException("No arguments provided");
}
for (Double value : values) {
if (value > 1 || value < 0) {
throw new IllegalArgumentException(
"Input is out of range [0,1]"
);
}
}

}

public static void validateFrequency(int freq, Double[] data){
if (freq <= 0){
throw new IllegalArgumentException("Value too low");
} else if (2*freq >= data.length) {
throw new ArrayIndexOutOfBoundsException("Frequency too high");
}

}
public static Container single_exponential_smoothing(Double[] data, Double alpha) {

validateRange(alpha);

int n = data.length;

Double[] pred = new Double[n];
Expand Down Expand Up @@ -186,12 +211,26 @@ public static Container single_exponential_smoothing(Double[] data, Double alpha
}

public static Container double_exponential_smoothing(Double[] data, Double alpha, Double beta) {

// Exception handling
validateRange(alpha, beta);

if (data == null || data.length < 3) {
throw new IllegalArgumentException("At least three observations are needed to calculate RMSE");
}

if (data[0] == null || data[1] == null || Double.isNaN(data[0]) || Double.isNaN(data[1])) {
throw new IllegalArgumentException("The first two observations must not be missing");
}

// Initializing variables
int n = data.length;

ArrayList<Double> pred = new ArrayList<>(n-1);
Double[] s = new Double[n-1];
Double[] b = new Double[n-1];

// Setting up s_0, b_0, y_0
s[0] = data[1];
b[0] = data[1] - data[0];
pred.add(s[0] + b[0]);
Expand All @@ -202,7 +241,9 @@ public static Container double_exponential_smoothing(Double[] data, Double alpha
ArrayList<Double> not_missing_pred = new ArrayList<>();
int n_size = 0;

// Iterative updating of the model
for (int i = 1; i < n-1; i++) {
// Imputes missing data
if (Double.isNaN(data[i + 1])) {
val = pred.get(i - 1);
} else {
Expand All @@ -211,11 +252,15 @@ public static Container double_exponential_smoothing(Double[] data, Double alpha

s[i] = alpha * val + (1 - alpha) * (s[i-1] + b[i-1]);
b[i] = beta * (s[i] - s[i-1]) + (1 - beta) * b[i-1];
pred.add(s[i] + b[i]);
}
if (i < n - 2){
pred.add(s[i] + b[i]);
}

}
pred.add(0, data[1]);
pred.add(0, data[0]);

// Initializing the data for calculation of RMSE
for (int i = 0; i < data.length; i++) {
if (!Double.isNaN(data[i])) {
not_missing.add(data[i]);
Expand All @@ -224,6 +269,7 @@ public static Container double_exponential_smoothing(Double[] data, Double alpha
}
}

// Calculation of RMSE
double sum = .0;
for (int i = 0; i < not_missing.size(); i++) {
sum += Math.pow(not_missing.get(i) - not_missing_pred.get(i), 2);
Expand All @@ -235,19 +281,29 @@ public static Container double_exponential_smoothing(Double[] data, Double alpha
}

public static Container triple_exponential_smoothing(Double[] data, Double alpha, Double beta,
Double gamma, Integer freq) {
Double gamma, Integer freq) {

// Exception handling
validateRange(alpha, beta, gamma);
validateFrequency(freq, data);

//Initialization data
double l = freq * 2;
ArrayList<Double> start_data = new ArrayList<>();

for (int i = 0; i < l; i++) {
start_data.add(data[i]);
}

// Building a centered moving average for the inititialization of the level variable
ArrayList<Double> filt = new ArrayList<>();
ArrayList<Double> trend = new ArrayList<>();

double len = freq;

// To set up the centered moving average filter, there is two cases, odd and even frequency.
// For the odd frequency, there is a true center point, so the filter weights are the same.
// For the even frequency, there is no center point, therefore, the edge weights are halved.
if (freq % 2 == 0) {
len = freq - 1;
}
Expand All @@ -261,9 +317,11 @@ public static Container triple_exponential_smoothing(Double[] data, Double alpha
filt.add(0.5/freq);
}

// Applying the CMA filter to the trend
double trend_len = l - filt.size() + 1;

for (int i = 0; i < l - trend_len; i++) {
// Goes through the start data, and sums the average trend over the first to seasons.
for (int i = 0; i < trend_len; i++) {
double sum = 0;
for (int j = i; j < i + filt.size(); j++) {
sum += start_data.get(j) * filt.get(j - i);
Expand All @@ -272,6 +330,7 @@ public static Container triple_exponential_smoothing(Double[] data, Double alpha
trend.add(sum);
}

// Removing the trend, to reveal the seasonal pattern
int cut = (int) (l - trend.size()) / 2;

ArrayList<Double> season_tmp = new ArrayList<>();
Expand All @@ -282,6 +341,7 @@ public static Container triple_exponential_smoothing(Double[] data, Double alpha

Double[] season = new Double[freq];

// Average by season position
for (int i = 0; i < freq; i++) {
double combined = 0;
if (i + freq < trend.size()) {
Expand All @@ -293,6 +353,7 @@ public static Container triple_exponential_smoothing(Double[] data, Double alpha
season[(i + (freq / 2)) % freq] = combined;
}

// Preparing the data for linear regression, in order to initialize level
double sum = 0;
for (int i = 0; i < season.length; i++) {
sum += season[i];
Expand All @@ -316,6 +377,8 @@ public static Container triple_exponential_smoothing(Double[] data, Double alpha

int n = data.length;


// Initalize arrays and create first forecast
double[] s = new double[n - freq];
s[0] = linreg.intercept();

Expand All @@ -338,6 +401,7 @@ public static Container triple_exponential_smoothing(Double[] data, Double alpha

int n_size = 0;

// Update the model per data point
for (int i = 1; i < n - freq; i++) {
if (Double.isNaN(data[i + freq - 1] )) {
val = pred.get(i - 1);
Expand All @@ -356,6 +420,7 @@ public static Container triple_exponential_smoothing(Double[] data, Double alpha
pred.add(i, data[i]);
}

// Initialize data for the RMSE calculation
for (int i = 0; i < data.length; i++) {
if (!Double.isNaN(data[i])) {
not_missing.add(data[i]);
Expand All @@ -364,6 +429,7 @@ public static Container triple_exponential_smoothing(Double[] data, Double alpha
}
}

// Calculate the RMSE
sum = .0;
for (int i = 0; i < not_missing.size(); i++) {
sum += Math.pow(not_missing.get(i) - not_missing_pred.get(i), 2);
Expand Down
151 changes: 151 additions & 0 deletions src/test/java/org/apache/sysds/runtime/util/EMAUtilsTest.java
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Original file line number Diff line number Diff line change
@@ -0,0 +1,151 @@
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/

package org.apache.sysds.runtime.util;

import org.apache.sysds.runtime.matrix.data.MatrixValue;
import org.apache.sysds.test.AutomatedTestBase;
import org.apache.sysds.test.TestConfiguration;
import org.junit.Assert;
import org.junit.Test;

import java.util.HashMap;

import static org.apache.sysds.test.TestUtils.readRMatrixFromFS;
import static org.apache.sysds.runtime.util.EMAUtils.*;

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public class EMAUtilsTest extends AutomatedTestBase {
private static final String TEST_NAME = "exponentialSmoothing";
private static final String TEST_DIR = "functions/builtin/";
private static final String TEST_CLASS_DIR = TEST_DIR + EMAUtilsTest.class.getSimpleName() + "/";

private static final double REFERENCE_EPS = 1e-4;
private static final Double[] TS_DATA = {
112., 118., 132., 129., 121., 135., 148., 148., 136., 119., 104., 118.,
115., 126., 141., 135., 125., 149., 170., 170., 158., 133., 114., 140.,
145., 150., 178., 163., 172., 178., 199., 199., 184., 162., 146., 166.,
171., 180., 193., 181., 183., 218., 230., 242., 209., 191., 172., 194.,
196., 196., 236., 235., 229., 243., 264., 272., 237., 211., 180., 201.
};
@Override
public void setUp() {
addTestConfiguration(TEST_NAME,
new TestConfiguration(TEST_CLASS_DIR, TEST_NAME, new String[] { "R_double", "R_triple", "R_triple_odd" }));
}

@Test
public void testDoubleExponentialSmoothing() {
fullRScriptName = SCRIPT_DIR + TEST_DIR + TEST_NAME + ".R";
String resultPath = output("R_double");

rCmd = getRCmd("double", resultPath);
runRScript(true);

HashMap<MatrixValue.CellIndex, Double> rOutput = readRMatrixFromFS(resultPath);
Assert.assertFalse("R output should contain values", rOutput.isEmpty());

int numRows = (int) rOutput.keySet().stream().mapToLong(idx -> idx.row).max().orElse(0);
double[] rFittedValues = new double[numRows];
for (int i = 1; i <= numRows; i++) {
rFittedValues[i - 1] = rOutput.get(new MatrixValue.CellIndex(i, 1));
}


Container javaFittedValues = double_exponential_smoothing(TS_DATA, 0.3, 0.1);

@ywcb00 ywcb00 Nov 2, 2026

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Could we change this test to perform exponential smoothing by calling a DML script rather than calling the Java method directly. Examples for such tests can be found in BuiltinPowerTransformTest.java and BuiltinEMATest.java.

Also, what is the difference between this test class and the BuiltinEMATest.java. Can we merge these two test classes?

@john-on-line27 john-on-line27 Nov 3, 2026

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Originally I could not find the test files. I can merge the two test files, however, the current test functions in BuiltinEMATest.java are failing, and since the scope of the thesis does not overlap with the subjects of the tests, it will be unlikely that I will have the time to fix them. @ywcb00


Assert.assertEquals("Array length mismatch", rFittedValues.length, javaFittedValues.values.length-2);
for (int i = 0; i < rFittedValues.length; i++) {
Assert.assertEquals("Mismatch at index " + i, rFittedValues[i], javaFittedValues.values[i+2], REFERENCE_EPS);
}
}

@Test
public void testTripleExponentialSmoothing(){
int freq = 12;
fullRScriptName = SCRIPT_DIR + TEST_DIR + TEST_NAME + ".R";
String resultPath = output("R_triple");

rCmd = getRCmd("triple", resultPath);
runRScript(true);

HashMap<MatrixValue.CellIndex, Double> rOutputTriple = readRMatrixFromFS(resultPath);
Assert.assertFalse(
"R output should contain values",
rOutputTriple.isEmpty()
);

int numRows = (int) rOutputTriple.keySet().stream()
.mapToLong(idx -> idx.row)
.max()
.orElse(0);

double[] rFittedValues = new double[numRows];

for (int i = 0; i < numRows; i++) {
rFittedValues[i] = rOutputTriple.getOrDefault(
new MatrixValue.CellIndex(i + 1, 1), 0.0);
}

Container javaResult = triple_exponential_smoothing(
TS_DATA, 0.3, 0.1, 0.2, freq);

Assert.assertEquals("Array length mismatch", rFittedValues.length, javaResult.values.length - freq);

for (int i = 0; i < rFittedValues.length; i++) {
Assert.assertEquals("Mismatch at index " + i, rFittedValues[i], javaResult.values[i + freq], REFERENCE_EPS);
}
}

@Test
public void testOddTripleExponentialSmoothing(){
int freq = 7;
fullRScriptName = SCRIPT_DIR + TEST_DIR + TEST_NAME + ".R";
String resultPath = output("R_triple_odd");

rCmd = getRCmd("odd_triple", resultPath);
runRScript(true);

HashMap<MatrixValue.CellIndex, Double> rOutputTriple = readRMatrixFromFS(resultPath);
Assert.assertFalse(
"R output should contain values",
rOutputTriple.isEmpty()
);

int numRows = (int) rOutputTriple.keySet().stream()
.mapToLong(idx -> idx.row)
.max()
.orElse(0);

double[] rFittedValues = new double[numRows];

for (int i = 0; i < numRows; i++) {
rFittedValues[i] = rOutputTriple.getOrDefault(
new MatrixValue.CellIndex(i + 1, 1), 0.0);
}

Container javaResult = triple_exponential_smoothing(
TS_DATA, 0.3, 0.1, 0.2, freq);

Assert.assertEquals("Array length mismatch", rFittedValues.length, javaResult.values.length - freq);

for (int i = 0; i < rFittedValues.length; i++) {
Assert.assertEquals("Mismatch at index " + i, rFittedValues[i], javaResult.values[i + freq], REFERENCE_EPS);
}
}
}
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