A small Java library for time-series analysis, forecasting, evaluation, and data-quality workflows.

• exponential smoothing: single, double, and triple
• moving averages: simple, cumulative, exponential, and weighted
• transformations: log, roots, Box-Cox, differencing, and seasonal differencing
• statistics: mean, variance, autocovariance, ACF, and PACF
• stationarity testing with Augmented Dickey-Fuller and KPSS
• model-based forecasting with ARIMA, SARIMA, and ARIMAX
• decomposition with STL-style trend/seasonal/remainder extraction
• compact state-space forecasting with a local-level Kalman model
• forecast evaluation: train/test splits, rolling-origin backtests, metrics, and Ljung-Box diagnostics
• prediction intervals for ARIMA, SARIMA, and local-level forecasts
• auto-selection helpers for ARIMA/SARIMA and exponential smoothing
• data-quality utilities for missing values, outliers, and winsorization
• release-hardening helpers: wrapper bootstrap scripts, publishing scaffolding, CI workflows, and model benchmark utilities

The project targets Java 17 and uses Gradle.

./gradlew test

This snapshot now includes a lightweight ./gradlew bootstrap script that reads gradle/wrapper/gradle-wrapper.properties, downloads the configured Gradle distribution when needed, and then runs the build. It is intended to keep CI and contributor onboarding working even without committing a generated gradle-wrapper.jar.

Primary implementation packages:

• tslib.collect
• tslib.dataquality
• tslib.decomposition
• tslib.diagnostics
• tslib.evaluation
• tslib.math
• tslib.model.arima
• tslib.model.expsmoothing
• tslib.model.statespace
• tslib.movingaverage
• tslib.selection
• tslib.tests
• tslib.transform
• tslib.util

Compatibility aliases added in this patch series:

• tslib.model.* forwards to tslib.model.expsmoothing.*
• tslib.model.ARIMA forwards to tslib.model.arima.ARIMA
• tslib.model.SARIMA forwards to tslib.model.arima.SARIMA
• tslib.model.ARIMAX forwards to tslib.model.arima.ARIMAX
• tslib.model.LocalLevelModel forwards to tslib.model.statespace.LocalLevelModel
• tslib.stats.Stats forwards to tslib.util.Stats

For a longer guide, see docs/MODEL_SELECTION_GUIDE.md.

import java.util.List;
import tslib.dataquality.MissingValueImputer;
import tslib.evaluation.BacktestResult;
import tslib.evaluation.IntervalForecast;
import tslib.evaluation.RollingOriginBacktest;
import tslib.model.ARIMA;
import tslib.model.ARIMAX;
import tslib.model.LocalLevelModel;
import tslib.selection.AutoArima;
import tslib.tests.KPSSTest;
import tslib.transform.Differencing;

List<Double> raw = MissingValueImputer.linearInterpolation(java.util.Arrays.asList(10.0, null, 12.0, 13.0, 14.0));
List<Double> diff = Differencing.difference(raw);
KPSSTest kpss = new KPSSTest(diff);

ARIMA arima = new ARIMA(1, 1, 0).fit(raw);
IntervalForecast intervals = arima.forecastWithIntervals(3, 0.95);

ARIMAX arimax = new ARIMAX(0, 0, 0).fit(
        List.of(7.0, 9.0, 11.0, 13.0),
        new double[][] {{1.0}, {2.0}, {3.0}, {4.0}});
List<Double> arimaxForecast = arimax.forecast(new double[][] {{5.0}, {6.0}});

RollingOriginBacktest backtest = new RollingOriginBacktest(4, 1);
BacktestResult result = backtest.run(raw, (train, horizon) -> new ARIMA(0, 1, 0).forecast(train, horizon));

AutoArima autoArima = new AutoArima(2, 1, 2, tslib.model.arima.ArimaOrderSearch.Criterion.AIC).fit(raw);
LocalLevelModel localLevel = new LocalLevelModel().fit(raw);

• tslib.transform.Differencing
• tslib.model.arima.ARIMA
• tslib.model.ARIMA

• tslib.model.arima.SARIMA
• tslib.model.arima.InformationCriteria
• tslib.model.arima.ArimaOrderSearch
• tslib.model.SARIMA

• tslib.decomposition.STLDecomposition
• tslib.tests.KPSSTest

• tslib.model.statespace.KalmanFilter
• tslib.model.statespace.LocalLevelModel
• tslib.model.LocalLevelModel

• tslib.evaluation.ForecastMetrics
• tslib.evaluation.TrainTestSplit
• tslib.evaluation.RollingOriginBacktest
• tslib.evaluation.BacktestResult
• tslib.diagnostics.LjungBoxTest

• tslib.evaluation.PredictionInterval
• tslib.evaluation.IntervalForecast
• interval support for ARIMA, SARIMA, and local-level models

• tslib.selection.AutoArima
• tslib.selection.AutoETS

• tslib.model.arima.ARIMAX
• tslib.model.ARIMAX

• tslib.dataquality.MissingValueImputer
• tslib.dataquality.OutlierDetector
• tslib.dataquality.Winsorizer

• release hardening via Gradle bootstrap scripts and publishing config
• CI + tag-based release workflows
• model-selection and release-checklist docs
• tslib.evaluation.ModelBenchmark
• tslib.evaluation.BenchmarkSummary

• release-candidate hardening via stronger edge-case and workflow tests
• README model chooser and testing guidance
• benchmark markdown/report helpers and benchmark-results template
• package-level Javadocs for primary public packages
• Java 17/21 CI matrix plus nightly verification workflow

This snapshot adds release-oriented repo plumbing:

• lightweight ./gradlew bootstrap scripts for Unix and Windows
• maven-publish, signing, and local build-repo publishing tasks
• CI and tag-driven release workflows under .github/workflows/
• docs/MODEL_SELECTION_GUIDE.md, docs/RELEASE_CHECKLIST.md, and docs/BENCHMARKING.md
• a small benchmark helper via tslib.evaluation.ModelBenchmark and BenchmarkSummary

For local validation:

./gradlew publishToMavenLocal
./gradlew publish

For tagged builds, see docs/RELEASE_CHECKLIST.md and .github/workflows/release.yml.

See the examples/ directory for runnable snippets:

• ForecastExample.java
• TransformExample.java
• ArimaExample.java
• SarimaExample.java
• StlAndKpssExample.java
• LocalLevelExample.java
• BacktestExample.java
• IntervalsExample.java
• AutoSelectionExample.java
• ArimaxExample.java
• DataQualityExample.java
• BenchmarkComparisonExample.java
• GenerateBenchmarkReportExample.java
• HotelBenchmarkComparisonExample.java

Run the full verification flow locally with:

./gradlew clean test jacocoTestReport javadoc

The console is configured to print individual test results. HTML reports are generated under build/reports/tests/test and build/reports/jacoco/test/html.

For a release candidate pass, also review docs/RELEASE_CHECKLIST.md, docs/TESTING_GUIDE.md, and docs/BENCHMARK_RESULTS.md.

The ARIMA/SARIMA, state-space, and evaluation additions are intentionally compact and dependency-light. They are designed for readable forecasting workflows inside this library rather than exhaustive econometric coverage.