How to Predict NaN (Missing Values) of a Dataframe Using ARIMA in Python?

Answer: Use ARIMA to model the time series excluding NaNs, then predict the missing values based on the fitted model and insert these predictions back into the original series.

Predicting missing values in a time series data using the ARIMA (AutoRegressive Integrated Moving Average) model involves several key steps. ARIMA is a popular method for time series forecasting, capable of capturing various statistical properties like trend and seasonality.

Steps to Predict Missing Values with ARIMA:

1. Prepare Data: Remove NaN values to create a clean time series dataset for modeling. This step is crucial as ARIMA models require continuous data without gaps.
2. Model Selection: Analyze the time series to determine the ARIMA model’s parameters (p, d, q) that best fit the data. This can be done using plots (ACF and PACF) or automatic selection methods available in Python libraries like pmdarima.
3. Model Fitting: Fit the ARIMA model to the cleaned time series data. Python’s statsmodels library can be used for this purpose, utilizing the ARIMA class and its fit() method.
4. Prediction: Once the model is fitted, predict the missing values for the original timestamps that had NaNs. The forecast or predict methods of the ARIMA model object can be used to generate predictions for the required time points.
5. Insert Predictions: Insert the predicted values back into the original dataframe at the positions of the missing values.

Conclusion:

Using ARIMA to predict missing values in a time series dataset is a practical approach, especially when the data exhibits clear trends or seasonal patterns. This method allows for the interpolation of missing data points based on the underlying patterns identified by the ARIMA model, thereby providing a statistically informed way to handle gaps in time series data. However, it’s important to ensure the chosen ARIMA model accurately captures the data’s characteristics for reliable predictions.