Cognite Charts - flag the far-off datapoints

Thanks @Knut Vidvei. Is there a way to use Outlier detection and visualise that in the same timeseries?

I am not able to understand and use that in CDF charts. 

Actually, It must look like the box plots and must show all the normal points and the outliers in a different color.

Hi @Knut Vidvei 

I would want to show that in the same chart itself. That is what my customer is asking for. If I have to work outside charts, I can use core Python and other visualization tools to show that details. 

For ex: this code will show it pretty well using core Python. 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Generate example time series data
np.random.seed(42)
n_points = 100
timestamps = pd.date_range(start='2023-01-01', periods=n_points, freq='D')
values = np.random.normal(loc=50, scale=10, size=n_points)
values[20] = 150  # Adding an outlier

data = pd.DataFrame({'Timestamp': timestamps, 'Value': values})

data['Z-Score'] = (data['Value'] - data['Value'].mean()) / data['Value'].std()

outlier_threshold = 2

outliers = data[data['Z-Score'].abs() > outlier_threshold]

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(data['Timestamp'], data['Value'], label='Time Series')
plt.scatter(outliers['Timestamp'], outliers['Value'], color='red', label='Outliers')
plt.xlabel('Timestamp')
plt.ylabel('Value')
plt.title('Time Series with Outliers')
plt.legend()
plt.grid(True)
plt.show()

The output will look like this for the above standard-python code snippet.

Please could you share a clear example with steps; I dont see any type or such setting in CDF charts in front-end.

Hi,

I zoomed in and highlighted the option with a red box here: