code_snippet.txt

import numpy as np
import pandas as pd
from orion import Orion

# Generate a synthetic time series dataset
np.random.seed(42)
time = np.arange(0, 1000, 1)
data = np.sin(time) + np.random.normal(0, 0.1, len(time))

# Introduce anomalies
data[200:210] += 3  # Add a spike
data[500:510] -= 3  # Add a dip

# Create a DataFrame
df = pd.DataFrame({'timestamp': time, 'value': data})
print(df.head())

# Initialize Orion
hyperparameters = {
    "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1": {
        "interval": 1
    },
    "keras.Sequential.LSTMTimeSeriesRegressor#1" : {
        "epochs": 5,
        "verbose": True
    }
}
orion = Orion("lstm_dynamic_threshold", hyperparameters)

# Fit the model and detect anomalies
anomalies = orion.fit_detect(df)

# Print detected anomalies
print("Detected anomalies:")
print(anomalies)