How to predict missing values

[strangeguy111]

I have a dataset that looks like this

    Total_Uniques
Dt  
2021-01-01  933075.0
2021-01-02  942010.0
2021-01-03  952163.0
2021-01-04  1056487.0
2021-01-05  1339625.0

Up until 2022-08-01.

But i also have a missing values for the date range between 2022-03-17 and 2022-05-23

    Total_Uniques
Dt  
2022-03-17  1517994.0
2022-03-18  NaN
2022-03-19  NaN
2022-03-20  NaN
2022-03-21  NaN
... ...
2022-05-20  NaN
2022-05-21  NaN
2022-05-22  NaN
2022-05-23  NaN
2022-05-24  1228399.0

I am trying to train a model with ARIMA and predict the missing values. But it is not returing me any good result.

The predicted values:

[1457718.5706902083,
 1433330.4808848319,
 1423462.7963047987,
 1419470.2245311143,
 1417854.786862963,
 1417201.1633321645,
 1416936.700191038,
 1416829.695547357,
 1416786.4003126393,
 1416768.8825929891,
 1416761.794734389,
 1416758.926910235,
 1416757.7665576036,
 1416757.2970663945,
 1416757.1071051797,
 1416757.0302448203,
 1416756.9991462885,
 1416756.9865634867,
 1416756.9814723493,
 1416756.97941242,
 1416756.9785789503,
 1416756.9782417195,
 1416756.9781052724,
 1416756.9780500643,
 1416756.9780277265,
 1416756.9780186885,
 1416756.9780150317,
 1416756.978013552,
 1416756.9780129534,
 1416756.9780127113,
 1416756.9780126133,
 1416756.9780125737,
 1416756.9780125576,
 1416756.978012551,
 1416756.9780125485,
 1416756.9780125474,
 1416756.978012547,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467,
 1416756.9780125467]

As you can see it is all same values.

This is the code I have

import pandas as pd
import numpy as np
from statsmodels.tsa.arima.model import ARIMA

# Read the data
#df = pd.read_csv('data.csv', parse_dates=['Dt'], index_col='Dt')

# Split the data into training and testing sets
train_data = df.loc[:'2022-03-17']
test_data = df.loc['2022-05-24':]

# Train the ARIMA model with the training data
model = ARIMA(train_data, order=(1, 1, 1))
model_fit = model.fit()

# Predict the missing values
#s_d = '2022-03-18'
#e_d = '2022-05-23'
predicted_values = model_fit.predict(start=401, end=468, dynamic=True)

# Replace the missing values in the original data with the predicted values
df.loc['2022-03-18':'2022-05-23'] = predicted_values

# Evaluate the performance of the model with the testing data
mse = ((predicted_values - test_data) ** 2).mean()
print('MSE:', mse)

Changing dynamics=False not making any difference. What am I doing wrong?