How to handle missing dates in an intermittent time series using Python?

I have an intermittent time series which is a type of time series where there are gaps in the data, i.e., some dates are missing. Filling in these missing values is important because it can help me perform analysis that would not be affected by missing values, I came across two methods provided by Pandas and they fill the missing dates either by the previous date or the next day but I also want to explore alternative methods for this, if there are some please provide them below.

The forward fill method:

This one fills the missing values with the last observed value in the series.

The backward fill method:

This one fills the missing values with the next observed value in the series.

Hey @mubashir_rizvi, to interpolate missing values in a time series, use NumPy’s interpolation method. Create a complete index of dates using the date_range function with a frequency of one day (freq='D' ). Use np.interp() with x-coordinates of the known data points (new index with all values), y-coordinates of known data points (series with missing dates), and x-coordinates of points to interpolate (values of the series). Create a new Pandas dataframe with interpolated values as the value column and dates in index as the index of the dataframe.

  • It allows for a wide variety of interpolation methods to be used, including linear, nearest-neighbor, polynomial, and spline interpolation.
  • Additionally, NumPy’s interpolation functions are generally faster and more efficient than similar functions in other libraries.

Hello @mubashir_rizvi , you can handle missing dates by using SciPy’s interpolation. Let me show you below:

The advantage of using the interp1d function is that it offers a more flexible and customizable approach to interpolating missing values compared to NumPy’s interp function.

It offers a choice of interpolation methods such as linear, quadratic, cubic, and more.

It allows for the extrapolation of values beyond the endpoints of the series, which is useful in cases where the data is expected to continue beyond the observed range.