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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteGenerate synthetic time-series data by first defining which temporal properties matter, then choosing a method that can represent them and validating the output against the real series. Matching value ranges or a histogram is not enough if the task depends on timing, autocorrelation, trends, seasonality, or relationships between variables.
What does it mean to preserve a time-series signal?
A time series is more than a collection of values: its observations arrive in sequence, and the order and spacing between them can carry information. Depending on the application, meaningful structure may include trends, seasonal cycles, periodic behavior, autocorrelation, abrupt events, missingness patterns, or relationships among multiple variables. Shuffling observations or matching only their overall distribution can erase that structure even when the synthetic values look plausible.
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There is no universal definition of a good synthetic series. A sequence intended to test code may need controlled edge cases; a dataset intended for model training may need realistic variation and useful downstream performance; data prepared for sharing also requires a separate disclosure-risk assessment. The UK Office for National Statistics (ONS) says evaluation depends on users’ needs, disclosure concerns, and available resources, and recommends checking validity and comparing important statistical properties. Read the ONS synthetic data pilot methodology.
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Choose a generation method that fits the data
Methods differ in what they assume, what they can vary, and how much representative data they need. The options below are documented approaches, not a universal ranking: tool documentation describes capabilities, not guaranteed fidelity on a particular dataset.
#1 Best Overall
| Method family | Examples and what they offer | Best fit | What to validate |
|---|---|---|---|
| Signal and stochastic-process construction | TimeSynth documents harmonic signals, Gaussian processes with several kernels, pseudoperiodic signals, autoregressive and continuous autoregressive processes, and NARMA. TimeSynth project documentation | Controlled scenarios where you can state the assumed generating mechanism, or test signals with known characteristics. | Whether the chosen assumptions represent the target system; a controllable signal is not automatically a realistic one. |
| Resampling and transformations | TSGM documentation describes moving-block bootstrap, jittering, scaling, magnitude warping, time warping, and DTW barycentric averaging among time-series augmentation approaches. TSGM documentation | Creating controlled variations around observed examples. | Whether each transformation preserves the invariants your task needs. For example, a time warp changes timing, so it may be inappropriate when exact event timing matters. |
| Learned sequential generators | TSGM describes data-driven and model-based approaches; the YData SDK documents a TimeSeriesSynthesizer intended to learn sequence patterns and relationships over time; YData-Synthetic documents TimeGAN as a GAN-based option. TSGM, YData SDK TimeSeriesSynthesizer, YData-Synthetic TimeGAN, version 1.3 documentation | Data-rich use cases where the relevant sequential patterns can be learned from representative examples. | Fit quality, coverage of unusual patterns, and whether relationships and downstream utility hold on the task at hand. |
| Frequency-domain surrogate generation | A 2025 preprint by Ofek Aloni, Gal Perelman, and Barak Fishbain proposes a discrete-Fourier-transform method with controllable similarity and reports analytical preservation of the first two statistical moments and autocorrelation. Read the preprint. | Exploring surrogate series when frequency-domain characteristics and similarity control are relevant. | The authors’ result is specific to their method; it does not establish preservation of every temporal feature or nonstationary structure. |
Before selecting among plausible methods, decide whether exact timing or irregular sampling matters, which trends, seasonal patterns, autocorrelation and cross-series relationships must survive, how rare events should be represented, and what source data and compute are available. Also consider whether performance on the intended downstream task needs to transfer. The cited sources do not establish universal acceptance thresholds or a benchmark ranking across these method families.
Set validation checks before generating data
Write down what the synthetic series must preserve before inspecting the output. A practical checklist, tailored to the intended use rather than a universal standard, can include:
- Values: valid ranges, marginal distributions, and any domain-specific bounds.
- Sequence behavior: autocorrelation at relevant lags, trends, seasonal or periodic behavior, and event timing.
- Relationships: cross-feature relationships, or per-entity patterns when observations belong to different entities.
- Data quality patterns: missingness and irregular sampling, if either affects the analysis or model.
- Coverage: variation across plausible regimes, rare events, and unusual but valid cases.
- Rules of the domain: constraints such as impossible values, invalid transitions, or mutually incompatible conditions.
These checks are a practical synthesis of documented temporal structure and ONS advice to prioritize properties important to users; no single cited source presents this exact list as a universal checklist. For broader context on time-series patterns, see YData SDK’s time-series overview.
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- State the use. Decide whether the data is for code testing, simulation, augmentation, forecasting or classification experiments, or sharing. “Good” depends on which of these jobs the sequences must do.
- Choose the checks and acceptance criteria. Match comparisons to the properties that matter. Set practical tolerances for your application rather than treating any one statistic as a universal pass mark; the cited guidance does not prescribe universal thresholds.
- Fit or configure the generator. Use representative input data for learned methods, or make assumptions explicit for constructed signals. Where a transformation changes timing, scale, or shape, decide whether that change is acceptable before applying it.
- Generate multiple sequences and inspect coverage. Look for varied, plausible regimes and events, not just repeated near-copies. Diversity across a dataset is a separate concern from whether any one generated sequence looks plausible. A 2021 MethodsX article presents a dedicated framework for validating diversity and reports work involving 50,000 diverse synthetic time series; that is a study-specific figure, not a recommended sample count. Read the article on validating diversity in synthetic time series.
- Compare synthetic and reference data. Use matched summaries and plots for the checks you chose, and test domain rules. ONS describes visual comparisons and differences in estimates as simple evaluation options; more complex modeling can be used to assess multivariate properties.
- Test task utility when relevant. If the purpose is model training or another downstream task, evaluate performance on that task as well as statistical resemblance. A dataset can look similar without being useful for the intended application.
- Assess disclosure risk separately before release. Similarity and utility results do not establish that synthetic records are safe to share. ONS treats disclosure assessment as case-specific and notes that plausible values can still be disclosive in longitudinal data.
How to interpret a validation result
Passing one check does not establish that the signal is preserved overall. Similar marginal distributions do not show that temporal dependence is right; similar autocorrelation does not prove the model captures event timing, nonstationarity, cross-variable behavior, or rare patterns. Read each result against the requirement it measures and report the properties that were not tested.
Likewise, a successful fit or a tool’s stated goals do not guarantee quality on your data. The YData SDK describes its TimeSeriesSynthesizer as learning sequence patterns and relationships, while TSGM and TimeSynth document different generation approaches. Treat those descriptions as capabilities to investigate, then validate the output on representative data and the exact task for which it will be used.
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