Python NAN Removal: Troubleshooting NANs in ARIMA Model Predictions

Previous PostIn , we analyzed S&P 500 stock data and predicted stock prices with an ARIMA model, but when comparing the predicted values to the actual values, we found that when calculating the mean squared error (MSE), the nan In this post, we'll use the Centering on the Python nanoremoval method, we'll discuss the common problems encountered in time series forecasting with ARIMA models using the nan Troubleshootingin the following steps.

What is a nan?

nanis "Not a Number"which occurs when an invalid value is generated during a calculation or when there are missing values in the data. Most commonly in time series analysis, when data is lost or there are incomplete values in the dataset, a nan values, in particular Pythonwhen doing time series analysis in , nan Using data that contains values can skew the performance of your predictive model or cause the computation to fail.

Top causes of NANs

  • Missing data: When the dataset contains some empty or invalid values. nan value is raised.
  • Invalid math: due to calculations with zero denominator, invalid data format, etc. nanmay occur.
  • Index mismatchIf the indexes of the predicted and actual values do not match, the calculation process will raise the nanmay occur.
파이썬 nan 제거 포스트 그림

The importance of removing Python NANs

In data forecasting tasks, such as time series analysis, you can use the nan If you train or evaluate a model with values included, the model may not work properly or produce incorrect results. By removing Python NANs, the Maintain the integrity of your data and improve the accuracy of your model predictions. For example, if you don't have price information for some days in your stock data, you might want to use the nan values, and if you don't remove them, your model won't be able to predict correctly.

Step-by-step guide to troubleshooting NAN

Now, the Remove Python nanfrom the ARIMA model prediction to the nan Let's take a step-by-step look at how to resolve the issue.

1. identify and remove missing data (NAN values)

First, you need to add a nan value is included and you need to remove it. Python's dropna() Functionallows you to remove the nan You can easily remove values.

Remove nan values from # S&P 500 close data
sp500_close_clean = sp500['Close'].dropna()

This code allows you to create a S&P 500 In the closing data, all nan You can remove values. You can only apply an ARIMA model when your data is clean.

2. Train an ARIMA model

Now train the ARIMA model using the cleaned data. nan Training a model based on data with values removed ensures that the prediction process is error-free.

from statsmodels.tsa.arima.model import ARIMA

Train the # ARIMA model
model = ARIMA(sp500_close_clean, order=(5, 1, 0))
model_fit = model.fit()

In this step, we'll use the nan Train an ARIMA model using the data with the values removed. Now that you've trained the model with clean data, you won't run into problems with subsequent predictions.

3. stock price forecast

After training the ARIMA model, we can predict the future stock price for 30 days. In the process, we also add the nan You must be careful not to include values.

# Stock price forecast for the next 30 days
forecast = model_fit.forecast(steps=30)

The predicted value includes nan value, which is used to estimate the future stock price. However, if the indexes of the predicted and actual values do not match, the nan You may encounter issues.

4. Index actual data to predicted values

Next, it's important to match the indexes of the predicted and actual data. If the indexes don't match, then when comparing predicted and actual values, the nanmay occur. Use the code below to align the indexes.

# Extract the last 30 days of actual data
sp500_last_30 = sp500_close_clean[-30:]

Set the index of the # forecast to the index of the actual data
forecast.index = sp500_last_30.index

This code ensures that the indexes of the predicted values and the actual data match, so that when comparing them, the nandoes not occur.

5. Calculate the mean squared error (MSE)

Now, the nan Now that we've resolved the issue, we need to check the Mean squared error (MSE)can be calculated normally.

1Calculate the # mean squared error (MSE)
mse = ((sp500_last_30 - forecast)**2).mean()
print(f'Mean Squared Error (MSE): {mse}')

This code allows you to create a nan You can calculate the MSE and evaluate the performance of your model without any problems.

Full modification code

import yfinance as yf
from statsmodels.tsa.arima.model import ARIMA
import matplotlib.pyplot as plt

Download the # S&P 500 data and remove NaN values
sp500 = yf.download('^GSPC', start='2010-01-01', end='2023-01-01')
sp500_close_clean = sp500['Close'].dropna()

Train a # ARIMA model
model = ARIMA(sp500_close_clean, order=(5, 1, 0))
model_fit = model.fit()

Forecast # 30 days into the future
forecast = model_fit.forecast(steps=30)

# Last 30 days actual data
sp500_last_30 = sp500_close_clean[-30:]

Set the index of the # forecast to the index of the actual data
forecast.index = sp500_last_30.index

Calculate the # mean squared error (MSE)
mse = ((sp500_last_30 - forecast)**2).mean()
print(f'Mean Squared Error (MSE): {mse}')

Print the # AIC value
print(f'AIC: {model_fit.aic}')
# Results
Mean squared error (MSE): 15599.163498498263
AIC: 31515.10069070282

Organize

In this post, we'll show you how to use the Python nan removal method to remove the nan We've covered the process of solving the problem. The dataset's nan The process of removing values and matching the predicted values to the indexes of the actual data is important, and it allows you to use the nan to accurately evaluate predictive models.

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