romanov73/time-series-smoothing
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#2 -- complete additive trent and seasonality

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This commit is contained in:
Anton Romanov 2020-11-03 11:38:53 +04:00
parent 6598df034d
commit f3614979bd
10 changed files with 137 additions and 120 deletions
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3
src/main/java/ru/ulstu/controllers/TimeSeriesController.java
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@ -10,7 +10,6 @@ import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import ru.ulstu.configurations.ApiConfiguration;
import ru.ulstu.models.Forecast;
import ru.ulstu.models.ForecastParams;
import ru.ulstu.models.TimeSeries;
import ru.ulstu.models.exceptions.ModelingException;
@ -52,7 +51,7 @@ public class TimeSeriesController {
@PostMapping("getForecast")
@ApiOperation("Получить прогноз временного ряда")
public ResponseEntity<Forecast> getForecastTimeSeries(@RequestBody ForecastParams forecastParams) throws ModelingException {
public ResponseEntity<TimeSeries> getForecastTimeSeries(@RequestBody ForecastParams forecastParams) throws ModelingException {
return new ResponseEntity<>(timeSeriesService.getForecast(forecastParams.getOriginalTimeSeries(),
forecastParams.getCountForecast()), HttpStatus.OK);
}

31
src/main/java/ru/ulstu/models/Forecast.java
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@ -1,31 +0,0 @@
package ru.ulstu.models;
public class Forecast {
private Model model;
private TimeSeries forecast;
public Forecast(Model model) {
this.model = model;
this.forecast = new TimeSeries("Forecast time series of '" + model.getOriginalTimeSeries().getName() + "'");
}
public Model getModel() {
return model;
}
public TimeSeries getForecastTimeSeries() {
return forecast;
}
public void addValue(TimeSeriesValue timeSeriesValue) {
forecast.addValue(timeSeriesValue);
}
@Override
public String toString() {
return "Forecast{" +
"model=" + model +
", forecast=" + forecast +
'}';
}
}

27
src/main/java/ru/ulstu/models/Model.java
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@ -1,27 +0,0 @@
package ru.ulstu.models;
public class Model {
private TimeSeries originalTimeSeries;
private TimeSeries modelTimeSeries;
public Model(TimeSeries originalTimeSeries) {
this.originalTimeSeries = originalTimeSeries;
this.modelTimeSeries = new TimeSeries("Model time series of '" + originalTimeSeries.getName() + "'");
}
public TimeSeries getOriginalTimeSeries() {
return originalTimeSeries;
}
public TimeSeries getModelTimeSeries() {
return modelTimeSeries;
}
public void addValue(TimeSeriesValue timeSeriesValue) {
modelTimeSeries.addValue(timeSeriesValue);
}
public void addValue(TimeSeriesValue basedOnValue, double value) {
modelTimeSeries.getValues().add(new TimeSeriesValue(basedOnValue.getDate(), value));
}
}

2
src/main/java/ru/ulstu/models/TimeSeries.java
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@ -53,7 +53,7 @@ public class TimeSeries {
}
public void addValue(TimeSeriesValue basedOnValue, Double value) {
values.add(new TimeSeriesValue(basedOnValue.getDate().plusDays(1), value));
values.add(new TimeSeriesValue(basedOnValue.getDate(), value));
}
public TimeSeriesValue getLastValue() {

4
src/main/java/ru/ulstu/pages/IndexView.java
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@ -48,7 +48,7 @@ public class IndexView implements Serializable {
LineChartSeries series2 = new LineChartSeries();
series2.setLabel("Сглаженный ряд");
try {
for (TimeSeriesValue value : timeSeriesService.getForecast(timeSeries, 10).getModel().getModelTimeSeries().getValues()) {
for (TimeSeriesValue value : timeSeriesService.smoothTimeSeries(timeSeries).getValues()) {
series2.set(DateTimeFormatter.ISO_LOCAL_DATE.format(value.getDate()), value.getValue());
}
} catch (ModelingException ex) {
@ -59,7 +59,7 @@ public class IndexView implements Serializable {
LineChartSeries series3 = new LineChartSeries();
series3.setLabel("Прогноз");
try {
for (TimeSeriesValue value : timeSeriesService.getForecast(timeSeries, 10).getForecastTimeSeries().getValues()) {
for (TimeSeriesValue value : timeSeriesService.getForecast(timeSeries, 5).getValues()) {
series3.set(DateTimeFormatter.ISO_LOCAL_DATE.format(value.getDate()), value.getValue());
}
} catch (ModelingException ex) {

20
src/main/java/ru/ulstu/services/TimeSeriesService.java
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@ -4,19 +4,20 @@ import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.stereotype.Service;
import ru.ulstu.TimeSeriesUtils;
import ru.ulstu.models.Forecast;
import ru.ulstu.models.TimeSeries;
import ru.ulstu.models.TimeSeriesValue;
import ru.ulstu.models.exceptions.ModelingException;
import ru.ulstu.tsMethods.exponential.AddTrendNoSeason;
import ru.ulstu.tsMethods.exponential.ExponentialMethodParams;
import ru.ulstu.tsMethods.exponential.ExponentialParamName;
import ru.ulstu.tsMethods.exponential.NoTrendNoSeason;
import java.time.LocalDateTime;
import java.util.Arrays;
import java.util.List;
import java.util.stream.Collectors;
import static ru.ulstu.tsMethods.exponential.ExponentialParamName.ALPHA;
import static ru.ulstu.tsMethods.exponential.ExponentialParamName.BETA;
@Service
public class TimeSeriesService {
@ -32,9 +33,16 @@ public class TimeSeriesService {
return ts;
}
public Forecast getForecast(TimeSeries timeSeries, int countPoints) throws ModelingException {
NoTrendNoSeason nn = new NoTrendNoSeason(ExponentialMethodParams.of(ExponentialParamName.ALPHA, 0.8));
return nn.getForecast(timeSeries, countPoints);
public TimeSeries getForecast(TimeSeries timeSeries, int countPoints) throws ModelingException {
//NoTrendNoSeason nn = new NoTrendNoSeason(ExponentialMethodParams.of(ExponentialParamName.ALPHA, 0.8));
AddTrendNoSeason an = new AddTrendNoSeason(timeSeries, ExponentialMethodParams.of(ALPHA, 0.8, BETA, 0.8));
return an.getForecast(countPoints);
}
public TimeSeries smoothTimeSeries(TimeSeries timeSeries) throws ModelingException {
//NoTrendNoSeason nn = new NoTrendNoSeason(timeSeries, ExponentialMethodParams.of(ExponentialParamName.ALPHA, 0.8));
AddTrendNoSeason an = new AddTrendNoSeason(timeSeries, ExponentialMethodParams.of(ALPHA, 0.8, BETA, 0.8));
return an.getModel();
}
public TimeSeries getTimeSeriesFromString(String tsString) {

65
src/main/java/ru/ulstu/tsMethods/TimeSeriesMethod.java
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@ -1,8 +1,6 @@
package ru.ulstu.tsMethods;
import ru.ulstu.TimeSeriesUtils;
import ru.ulstu.models.Forecast;
import ru.ulstu.models.Model;
import ru.ulstu.models.TimeSeries;
import ru.ulstu.models.TimeSeriesValue;
import ru.ulstu.models.exceptions.ForecastValidateException;
@ -15,44 +13,44 @@ import java.time.temporal.ChronoUnit;
* Наиболее общая логика моделировани и прогнозирования временных рядов
*/
public abstract class TimeSeriesMethod {
protected TimeSeries originalTimeSeries;
private TimeSeries model;
public TimeSeriesMethod(TimeSeries originalTimeSeries) throws ModelingException {
this.originalTimeSeries = originalTimeSeries;
}
/**
* Возвращает модельное представление временного ряда: для тех же точек времени что и в параметре timeSeries
* строится модель. Количество точек может быть изменено: сокращено при сжатии ряда, увеличено при интерполяции.
* Метод является шаблонным, выполняет операции валидации исходного ряда и потом его моделирование
*
* @param timeSeries исходный временной ряд подлежащий моделированию
* @return модель временного ряда
* @throws TimeSeriesValidateException
*/
public Model getModel(TimeSeries timeSeries) throws ModelingException {
validateTimeSeries(timeSeries);
return getModelOfValidTimeSeries(timeSeries);
protected void makeModel() throws ModelingException {
validateTimeSeries();
model = getModelOfValidTimeSeries();
}
/**
* Возвращает модельное представление валидного временного ряда: для тех же точек времени что и в параметре timeSeries
* строится модель. Количество точек может быть изменено: сокращено при сжатии ряда, увеличено при интерполяции.
*
* @param timeSeries исходный временной ряд подлежащий моделированию
* @return
*/
protected abstract Model getModelOfValidTimeSeries(TimeSeries timeSeries) throws ModelingException;
protected abstract TimeSeries getModelOfValidTimeSeries() throws ModelingException;
/**
* Выполняет построение прогноза временного ряда. Даты спрогнозированных точек будут сгенерированы по модельным точкам.
*
* @param model модель временного ряда
* @param countPoints количество точек для прогнозирования
* @return прогноз временного ряда
*/
public Forecast getForecast(Model model, int countPoints) throws ModelingException {
Forecast forecast = new Forecast(model);
forecast = generateEmptyForecastPoints(forecast, countPoints);
public TimeSeries getForecastWithValidParams(int countPoints) throws ModelingException {
TimeSeries forecast = generateEmptyForecastPoints(originalTimeSeries, countPoints);
forecast.getFirstValue().setValue(getModel().getLastValue().getValue());
forecast = makeForecast(forecast);
if (!forecast.getForecastTimeSeries().getFirstValue()
.equals(forecast.getModel().getModelTimeSeries().getLastValue())) {
throw new ForecastValidateException("Первая точка прогноза должна совпадать с последней модельной точкой");
}
return forecast;
}
@ -62,15 +60,14 @@ public abstract class TimeSeriesMethod {
* @param forecast Заготовка прогноза временного ряда с пустыми значениями
* @return
*/
protected abstract Forecast makeForecast(Forecast forecast);
protected abstract TimeSeries makeForecast(TimeSeries forecast) throws ModelingException;
protected Forecast generateEmptyForecastPoints(Forecast forecast, int countPointForecast) {
long diffMilliseconds = TimeSeriesUtils.getTimeDifferenceInMilliseconds(forecast.getModel().getOriginalTimeSeries());
forecast.getForecastTimeSeries()
.addValue(new TimeSeriesValue(forecast.getModel().getModelTimeSeries().getLastValue().getDate()));
protected TimeSeries generateEmptyForecastPoints(TimeSeries modelTimeSeries, int countPointForecast) {
long diffMilliseconds = TimeSeriesUtils.getTimeDifferenceInMilliseconds(originalTimeSeries);
TimeSeries forecast = new TimeSeries("Forecast of " + originalTimeSeries.getName());
forecast.addValue(new TimeSeriesValue(modelTimeSeries.getLastValue().getDate()));
for (int i = 1; i < countPointForecast + 1; i++) {
forecast.getForecastTimeSeries()
.addValue(new TimeSeriesValue(forecast.getForecastTimeSeries().getValues().get(i - 1).getDate().plus(diffMilliseconds, ChronoUnit.MILLIS)));
forecast.addValue(new TimeSeriesValue(forecast.getValues().get(i - 1).getDate().plus(diffMilliseconds, ChronoUnit.MILLIS)));
}
return forecast;
}
@ -79,13 +76,12 @@ public abstract class TimeSeriesMethod {
* Выполняет построение модели и прогноза временного ряда. Даты спрогнозированных точек будут сгенерированы
* по модельным точкам.
*
* @param timeSeries временной ряда
* @param countPoints количество точек для прогнозирования
* @return прогноз временного ряда
*/
public Forecast getForecast(TimeSeries timeSeries, int countPoints) throws ModelingException {
public TimeSeries getForecast(int countPoints) throws ModelingException {
validateForecastParams(countPoints);
return getForecast(getModel(timeSeries), countPoints);
return getForecastWithValidParams(countPoints);
}
private void validateForecastParams(int countPoints) throws ForecastValidateException {
@ -94,18 +90,25 @@ public abstract class TimeSeriesMethod {
}
}
private void validateTimeSeries(TimeSeries timeSeries) throws TimeSeriesValidateException {
if (timeSeries == null || timeSeries.isEmpty()) {
private void validateTimeSeries() throws TimeSeriesValidateException {
if (originalTimeSeries == null || originalTimeSeries.isEmpty()) {
throw new TimeSeriesValidateException("Временной ряд должен быть не пустым");
}
if (timeSeries.getLength() < 2) {
if (originalTimeSeries.getLength() < 2) {
throw new TimeSeriesValidateException("Временной ряд должен содержать хотя бы 2 точки");
}
if (timeSeries.getValues().stream().anyMatch(val -> val == null || val.getValue() == null)) {
if (originalTimeSeries.getValues().stream().anyMatch(val -> val == null || val.getValue() == null)) {
throw new TimeSeriesValidateException("Временной ряд содержит пустые значения");
}
if (timeSeries.getValues().stream().anyMatch(val -> val.getDate() == null)) {
if (originalTimeSeries.getValues().stream().anyMatch(val -> val.getDate() == null)) {
throw new TimeSeriesValidateException("Временной ряд должен иметь отметки времени");
}
}
public TimeSeries getModel() throws ModelingException {
if (model == null) {
makeModel();
}
return model;
}
}

61
src/main/java/ru/ulstu/tsMethods/exponential/AddTrendNoSeason.java Normal file
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@ -0,0 +1,61 @@
package ru.ulstu.tsMethods.exponential;
import ru.ulstu.models.TimeSeries;
import ru.ulstu.models.exceptions.ModelingException;
import ru.ulstu.tsMethods.TimeSeriesMethod;
import java.util.ArrayList;
import java.util.List;
import static ru.ulstu.tsMethods.exponential.ExponentialParamName.ALPHA;
import static ru.ulstu.tsMethods.exponential.ExponentialParamName.BETA;
public class AddTrendNoSeason extends TimeSeriesMethod {
private final ExponentialMethodParams exponentialMethodParams;
private final List<Double> sComponent = new ArrayList<>();
private final List<Double> tComponent = new ArrayList<>();
public AddTrendNoSeason(TimeSeries timeSeries, ExponentialMethodParams exponentialMethodParams) throws ModelingException {
super(timeSeries);
this.exponentialMethodParams = exponentialMethodParams;
}
@Override
protected TimeSeries getModelOfValidTimeSeries() throws ModelingException {
sComponent.clear();
tComponent.clear();
sComponent.add(originalTimeSeries.getFirstValue().getValue());
tComponent.add(originalTimeSeries.getValues().get(1).getValue() - originalTimeSeries.getValues().get(0).getValue());
TimeSeries model = new TimeSeries("Model of " + originalTimeSeries.getName());
model.addValue(originalTimeSeries.getFirstValue());
//выполняется проход модели по сглаживанию
for (int t = 1; t < originalTimeSeries.getValues().size(); t++) {
sComponent.add(exponentialMethodParams.getValue(ALPHA) * originalTimeSeries.getNumericValue(t)
+ (1 - exponentialMethodParams.getValue(ALPHA))
* (sComponent.get(t - 1) - tComponent.get(t - 1)));
tComponent.add(exponentialMethodParams.getValue(BETA)
* (sComponent.get(t) - sComponent.get(t - 1))
+ (1 - exponentialMethodParams.getValue(BETA)) * tComponent.get(t - 1));
model.addValue(originalTimeSeries.getValues().get(t), sComponent.get(sComponent.size() - 1));
}
return model;
}
@Override
protected TimeSeries makeForecast(TimeSeries forecast) throws ModelingException {
for (int t = 1; t < forecast.getLength(); t++) {
/*int indexOffsetForModel = t + getModel().getLength() - 2;
sComponent.add(exponentialMethodParams.getValue(ALPHA) * forecast.getNumericValue(t-1)
+ (1 - exponentialMethodParams.getValue(ALPHA))
* (sComponent.get(indexOffsetForModel) - tComponent.get(indexOffsetForModel - 1)));
tComponent.add(exponentialMethodParams.getValue(BETA)
* (sComponent.get(indexOffsetForModel) - sComponent.get(indexOffsetForModel - 1))
+ (1 - exponentialMethodParams.getValue(BETA)) * tComponent.get(indexOffsetForModel - 1));*/
forecast.getValues().get(t).setValue(sComponent.get(sComponent.size() - 1) + tComponent.get(tComponent.size() - 1) * t);
}
return forecast;
}
}

4
src/main/java/ru/ulstu/tsMethods/exponential/ExponentialMethodParams.java
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@ -23,4 +23,8 @@ public class ExponentialMethodParams {
public static ExponentialMethodParams of(ExponentialParamName param1, Double value1) {
return new ExponentialMethodParams(ImmutableMap.of(param1, value1));
}
public static ExponentialMethodParams of(ExponentialParamName param1, Double value1, ExponentialParamName param2, Double value2) {
return new ExponentialMethodParams(ImmutableMap.of(param1, value1, param2, value2));
}
}

40
src/main/java/ru/ulstu/tsMethods/exponential/NoTrendNoSeason.java
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@ -1,43 +1,43 @@
package ru.ulstu.tsMethods.exponential;
import ru.ulstu.models.Forecast;
import ru.ulstu.models.Model;
import ru.ulstu.models.TimeSeries;
import ru.ulstu.models.exceptions.ModelingException;
import ru.ulstu.tsMethods.TimeSeriesMethod;
import java.util.ArrayList;
import java.util.List;
import static ru.ulstu.tsMethods.exponential.ExponentialParamName.ALPHA;
public class NoTrendNoSeason extends TimeSeriesMethod {
private ExponentialMethodParams exponentialMethodParams;
private final ExponentialMethodParams exponentialMethodParams;
private final List<Double> sComponent = new ArrayList<>();
public NoTrendNoSeason(ExponentialMethodParams exponentialMethodParams) {
public NoTrendNoSeason(TimeSeries timeSeries, ExponentialMethodParams exponentialMethodParams) throws ModelingException {
super(timeSeries);
this.exponentialMethodParams = exponentialMethodParams;
}
@Override
protected Model getModelOfValidTimeSeries(TimeSeries timeSeries) throws ModelingException {
Model model = new Model(timeSeries);
model.addValue(timeSeries.getFirstValue());
protected TimeSeries getModelOfValidTimeSeries() throws ModelingException {
sComponent.clear();
sComponent.add(originalTimeSeries.getFirstValue().getValue());
TimeSeries model = new TimeSeries("Model of " + originalTimeSeries.getName());
model.addValue(originalTimeSeries.getFirstValue());
//выполняется проход модели по сглаживанию
for (int t = 1; t < timeSeries.getValues().size(); t++) {
model.addValue(timeSeries.getValues().get(t),
(1 - exponentialMethodParams.getValue(ALPHA)) * timeSeries.getNumericValue(t)
+ exponentialMethodParams.getValue(ALPHA) * model.getModelTimeSeries().getValues().get(t - 1).getValue());
for (int t = 1; t < originalTimeSeries.getValues().size(); t++) {
sComponent.add(sComponent.get(t - 1)
+ exponentialMethodParams.getValue(ALPHA)
* (originalTimeSeries.getNumericValue(t) - sComponent.get(t - 1)));
model.addValue(originalTimeSeries.getValues().get(t), sComponent.get(sComponent.size() - 1));
}
return model;
}
@Override
protected Forecast makeForecast(Forecast forecast) {
forecast.getForecastTimeSeries()
.getFirstValue()
.setValue(forecast.getModel().getModelTimeSeries().getLastValue().getValue());
for (int t = 1; t < forecast.getForecastTimeSeries().getLength(); t++) {
forecast.getForecastTimeSeries()
.getValues()
.get(t)
.setValue(forecast.getForecastTimeSeries().getValues().get(t - 1).getValue());
protected TimeSeries makeForecast(TimeSeries forecast) {
for (int t = 1; t < forecast.getLength(); t++) {
forecast.getValues().get(t).setValue(forecast.getValues().get(t - 1).getValue());
}
return forecast;
}