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time-series
time-series-decomposition
benchmark
component-recovery
symbolic-regression
icml-2026
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17b7ba4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | """Plotting utilities for synthetic_ts_bench."""
from __future__ import annotations
from typing import Any, Dict
import matplotlib.pyplot as plt
import numpy as np
def plot_series_components(series_dict: Dict[str, Any], title: str = "") -> None:
"""
Plot observed series plus decomposed components.
Args:
series_dict: Output of ``generate_series``.
title: Optional figure title.
"""
t = series_dict["t"]
y = series_dict["y"]
trend = series_dict["trend"]
season = series_dict["season"]
clean = series_dict["clean"]
events = series_dict["events"]
noise = series_dict["noise"]
fig, axes = plt.subplots(4, 1, figsize=(10, 8), sharex=True)
axes[0].plot(t, y, label="Observed y", color="black")
axes[0].set_ylabel("y")
axes[0].legend(loc="upper right")
axes[1].plot(t, trend, label="Trend", color="tab:blue")
axes[1].plot(t, season, label="Season", color="tab:orange", alpha=0.8)
axes[1].set_ylabel("Trend/Season")
axes[1].legend(loc="upper right")
axes[2].plot(t, clean, label="Clean (T+S+E)", color="tab:green")
axes[2].plot(t, y, label="Observed", color="black", alpha=0.3)
axes[2].set_ylabel("Clean vs y")
axes[2].legend(loc="upper right")
axes[3].plot(t, events, label="Events", color="tab:red")
axes[3].plot(t, noise, label="Noise", color="tab:purple", alpha=0.7)
axes[3].set_ylabel("Events/Noise")
axes[3].set_xlabel("Time")
axes[3].legend(loc="upper right")
fig.suptitle(title or "Synthetic series components")
fig.tight_layout()
fig.subplots_adjust(top=0.92)
def plot_power_spectrum(
series_dict: Dict[str, Any],
component_key: str = "y",
title: str = "Power spectrum",
) -> None:
"""
Plot a simple power spectrum (FFT magnitude) of a chosen component.
Args:
series_dict: Output of ``generate_series``.
component_key: Key to pick from the dict, e.g. ``"season"``.
title: Plot title.
"""
signal = np.asarray(series_dict.get(component_key))
if signal is None:
raise ValueError(f"Component '{component_key}' not found in series_dict.")
t = series_dict["t"]
dt = t[1] - t[0] if len(t) > 1 else 1.0
freq = np.fft.rfftfreq(signal.size, d=dt)
power = np.abs(np.fft.rfft(signal)) ** 2
plt.figure(figsize=(8, 4))
plt.plot(freq, power)
plt.xlabel("Frequency")
plt.ylabel("Power")
plt.title(title)
plt.tight_layout()
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