> ## Documentation Index > Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt > Use this file to discover all available pages before exploring further. # Compose datasets with SynSet `SynSet` combines several generators into one long-format panel — the way to build a dataset that *mixes* behaviors (say, trending random walks alongside daily-seasonal demand) instead of repeating one shape. Each generator contributes a batch of series, and their ids are numbered sequentially so the result stays a single, model-ready frame. > **SynSet vs. balanced\_pool vs. generate\_series** > > `SynSet` is the low-level composer you control directly. > [`balanced_pool`](balanced_pool) is a ready-made list of 42 generators > you can drop into a `SynSet`, and > [`generate_series`](../getting-started/quickstart) wraps that pool in > a one-liner. Use `SynSet` when you want an explicit, curated mix of > generators. ```python theme={null} import matplotlib.pyplot as plt import polars as pl from synforecast import SynSet from synforecast.generators import RandomWalkGenerator, SeasonalGenerator ``` ## Define generators Create a random walk generator and a seasonal generator with different parameter configurations. ```python theme={null} rw_params = { "min_length": 100, "max_length": 150, "freq": "h", "drift": 0.1, "volatility": 1.5, "start_value": 100.0, "seed": 42, } seasonal_params = { "min_length": 100, "max_length": 150, "freq": "h", "seasonality_period": 24, "seasonality_amplitude": 15.0, "trend": 0.05, "noise_level": 2.0, "base_level": 50.0, "seed": 123, } rw_gen = RandomWalkGenerator(engine="polars", **rw_params) seasonal_gen = SeasonalGenerator(engine="polars", **seasonal_params) ``` ## Generate the dataset `generate(n_series_per_generator=3)` draws 3 series from each generator — 6 in total. Ids are assigned in generator order: `0-2` are the random walks, `3-5` the seasonal series. Pass `start_id` to offset the numbering when stitching several datasets together. ```python theme={null} dataset = SynSet([rw_gen, seasonal_gen]) df = dataset.generate(n_series_per_generator=3) print(f"Generated {df['unique_id'].n_unique()} time series") print(f"Total observations: {len(df)}") df.head(10) ``` ```text theme={null} Generated 6 time series Total observations: 740 ``` | unique\_id | ds | y | | ---------- | ------------------- | ---------- | | cat | datetime\[ns] | f64 | | "0" | 2000-01-01 00:00:00 | 100.620958 | | "0" | 2000-01-01 01:00:00 | 102.500707 | | "0" | 2000-01-01 02:00:00 | 101.104897 | | "0" | 2000-01-01 03:00:00 | 100.564106 | | "0" | 2000-01-01 04:00:00 | 99.076724 | | "0" | 2000-01-01 05:00:00 | 97.674284 | | "0" | 2000-01-01 06:00:00 | 96.714608 | | "0" | 2000-01-01 07:00:00 | 97.294935 | | "0" | 2000-01-01 08:00:00 | 98.929896 | | "0" | 2000-01-01 09:00:00 | 97.918173 | ```python theme={null} fig, ax = plt.subplots(figsize=(12, 5)) unique_ids = df["unique_id"].unique().to_list() for uid in unique_ids: series = df.filter(pl.col("unique_id") == uid) # IDs 0, 1, 2 are RandomWalk; IDs 3, 4, 5 are Seasonal. gen_type = "RandomWalk" if int(uid) < 3 else "Seasonal" ax.plot(series["ds"].to_list(), series["y"].to_list(), label=f"{uid} ({gen_type})", alpha=0.8) ax.set_title("SynSet dataset: random-walk and seasonal generators") ax.set_xlabel("Timestamp") ax.set_ylabel("Value") ax.legend(fontsize=8) plt.tight_layout() plt.show() ``` ## Statistics by series Compare summary statistics across all generated series. ```python theme={null} stats = ( df.group_by("unique_id") .agg( [ pl.col("y").count().alias("count"), pl.col("y").min().alias("min_value"), pl.col("y").max().alias("max_value"), pl.col("y").mean().alias("mean_value"), pl.col("y").std().alias("std_value"), ] ) .sort("unique_id") ) stats ``` | unique\_id | count | min\_value | max\_value | mean\_value | std\_value | | ---------- | ----- | ---------- | ---------- | ----------- | ---------- | | cat | u32 | f64 | f64 | f64 | f64 | | "0" | 104 | 94.028521 | 110.129544 | 100.735208 | 3.266454 | | "1" | 139 | 100.090121 | 151.940789 | 118.881905 | 13.27846 | | "2" | 133 | 89.176748 | 109.298167 | 100.537712 | 5.13869 | | "3" | 100 | 35.600282 | 70.029241 | 53.290106 | 10.264408 | | "4" | 134 | 34.431384 | 74.281368 | 54.466225 | 10.722442 | | "5" | 130 | 32.718865 | 74.773261 | 54.063785 | 11.005692 | ## Sample data Compare the first few rows from a random walk series and a seasonal series. ```python theme={null} print("Sample of series 0 (Random Walk - first 10 rows):") df.filter(pl.col("unique_id") == "0").head(10) ``` ```text theme={null} Sample of series 0 (Random Walk - first 10 rows): ``` | unique\_id | ds | y | | ---------- | ------------------- | ---------- | | cat | datetime\[ns] | f64 | | "0" | 2000-01-01 00:00:00 | 100.620958 | | "0" | 2000-01-01 01:00:00 | 102.500707 | | "0" | 2000-01-01 02:00:00 | 101.104897 | | "0" | 2000-01-01 03:00:00 | 100.564106 | | "0" | 2000-01-01 04:00:00 | 99.076724 | | "0" | 2000-01-01 05:00:00 | 97.674284 | | "0" | 2000-01-01 06:00:00 | 96.714608 | | "0" | 2000-01-01 07:00:00 | 97.294935 | | "0" | 2000-01-01 08:00:00 | 98.929896 | | "0" | 2000-01-01 09:00:00 | 97.918173 | ```python theme={null} print("Sample of series 3 (Seasonal - first 10 rows):") df.filter(pl.col("unique_id") == "3").head(10) ``` ```text theme={null} Sample of series 3 (Seasonal - first 10 rows): ``` | unique\_id | ds | y | | ---------- | ------------------- | --------- | | cat | datetime\[ns] | f64 | | "3" | 2000-01-01 00:00:00 | 48.634355 | | "3" | 2000-01-01 01:00:00 | 51.658968 | | "3" | 2000-01-01 02:00:00 | 56.909722 | | "3" | 2000-01-01 03:00:00 | 59.2974 | | "3" | 2000-01-01 04:00:00 | 62.751023 | | "3" | 2000-01-01 05:00:00 | 65.499531 | | "3" | 2000-01-01 06:00:00 | 67.897628 | | "3" | 2000-01-01 07:00:00 | 63.845217 | | "3" | 2000-01-01 08:00:00 | 65.0017 | | "3" | 2000-01-01 09:00:00 | 62.627031 |