Support for Time series Visualization

uadapy/__init__.py

@@ -1,3 +1,4 @@
 from .distribution import Distribution
+from .timeseries import TimeSeries, CorrelatedDistributions
 
-__all__ = ['Distribution']
+__all__ = ['Distribution','TimeSeries', 'CorrelatedDistributions']

uadapy/data/data.py

@@ -1,6 +1,7 @@
 from sklearn import datasets
 import numpy as np
-from uadapy import Distribution
+from uadapy import Distribution, TimeSeries
+from scipy import stats
 
 def load_iris_normal():
     """
@@ -23,3 +24,48 @@ def load_iris():
     for c in np.unique(iris.target):
         dist.append(Distribution(np.array(iris.data[iris.target == c])))
     return dist
+
+def generate_synthetic_timeseries(timesteps=200):
+    """
+    Generates synthetic time series data by modeling a combination of trend,
+    periodic patterns, and noise using a multivariate normal distribution
+    with an exponential quadratic kernel for covariance.
+
+    Parameters
+    ----------
+    timesteps : int
+        The time steps of the time series.
+        Default value is 200.
+
+    Returns
+    -------
+    timeseries : Timeseries object
+        An instance of the TimeSeries class, which represents a univariate time series.
+    """
+    np.random.seed(0)
+    t = np.arange(1, timesteps + 1)
+    trend = t / 10
+    periodic = 10 * np.sin(2 * np.pi * t / 100)
+    noise = 2 * (np.random.rand(timesteps) - 0.5)
+    mu = trend + periodic + noise
+    sigma2 = 20 * np.ones(timesteps)
+    sigma_sq = np.sqrt(sigma2)
+    sigma = np.zeros((timesteps, timesteps))
+
+    def ex_qu_kernel(x, y, sigma_i, sigma_j, l):
+        return sigma_i * sigma_j * np.exp(-0.5 * np.linalg.norm(x - y)**2 / l**2)
+
+    for i in range(timesteps):
+        for j in range(timesteps):
+            sigma[i, j] = ex_qu_kernel(t[i], t[j], sigma_sq[i], sigma_sq[j], 5)
+
+    # Ensure symmetry
+    sigma = (sigma + sigma.T) / 2
+
+    # Ensure positive definiteness
+    epsilon = 1e-6
+    sigma += np.eye(sigma.shape[0]) * epsilon
+    model = stats.multivariate_normal(mu, sigma)
+    timeseries = TimeSeries(model, timesteps)
+
+    return timeseries