predict next 5 minutes of a trendline in python

main.py
from sklearn.linear_model import LinearRegression
import numpy as np

# Assuming your data is in a numpy array of shape (n_samples, 2),
# where the first column is the timestamp and the second is the value

# First, compute the time differences between consecutive samples
timestamps = data[:, 0]
timesincelast = np.diff(timestamps) / 1000  # convert to seconds

# Then, compute the derivative of the trendline
values = data[:, 1]
derivatives = np.diff(values) / timesincelast

# Finally, fit a linear regression model to the derivatives
model = LinearRegression()
model.fit(derivatives[:-1, np.newaxis], derivatives[1:])

# Predict the next derivative
next_derivative = model.predict(derivatives[-1, np.newaxis])

# Use the prediction to compute the next value
next_value = values[-1] + next_derivative * timesincelast[-1]

print("Predicted value in 5 minutes:", next_value)
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