What are the best ways to handle missing time-series data for predictive analytics?

1 What is missing time-series data?

Missing time-series data is a common problem in data engineering, where some values of a variable are not recorded or observed over time. This can happen due to various reasons, such as sensor failures, data transmission errors, human errors, or intentional omission. Missing time-series data can affect the quality and reliability of predictive analytics, as it can introduce bias, reduce accuracy, and increase uncertainty.

2 How to impute missing time-series data?

Imputation is one of the most widely used methods to handle missing time-series data for predictive analytics. Imputation involves replacing the missing values with some estimated values, based on the available data. There are different types of imputation methods, such as mean, median, mode, last observation carried forward, or linear regression. The advantage of imputation is that it preserves the original shape and size of the data, and it is easy to implement. However, imputation can also introduce noise, distortion, and overfitting, and it can ignore the temporal structure and patterns of the data.

3 How to interpolate missing time-series data?

Interpolation is another common method to handle missing time-series data for predictive analytics. Interpolation involves creating new values that fit between the existing values, based on a mathematical function or a curve. There are different types of interpolation methods, such as linear, spline, polynomial, or piecewise constant. The advantage of interpolation is that it can capture the smoothness and continuity of the data, and it can preserve the temporal structure and patterns of the data. However, interpolation can also be sensitive to outliers, extrapolation, and non-linearity, and it can require more computational resources.

4 How to model missing time-series data?

Modeling is a more advanced method to handle missing time-series data for predictive analytics. Modeling involves using a statistical or machine learning model to generate or simulate the missing values, based on the underlying distribution and dynamics of the data. There are different types of modeling methods, such as state-space models, autoregressive models, or neural networks. The advantage of modeling is that it can account for the complexity and uncertainty of the data, and it can incorporate prior knowledge and domain expertise. However, modeling can also be difficult to implement, validate, and interpret, and it can require more data and assumptions.

5 How to choose the best method for your data engineering project?

There is no one-size-fits-all solution to handle missing time-series data for predictive analytics. The best method depends on several factors, such as the amount and pattern of missingness, the characteristics and quality of the data, the objective and scope of the analysis, and the available resources and tools. Therefore, you should always evaluate the pros and cons of each method, and compare the results and performance of different methods, before choosing the most suitable one for your data engineering project.

6 How to implement the best method in Python?

Python is a popular programming language for data engineering and predictive analytics, as it offers a variety of libraries and packages that can help you handle missing time-series data. Some of the most useful ones are pandas, numpy, scipy, sklearn, and statsmodels. You can use these libraries to perform different operations, such as detecting, removing, imputing, interpolating, and modeling missing time-series data, and to visualize and analyze the results. For example, you can use the isna() function in pandas to check for missing values, the fillna() function in pandas to impute missing values, the interpolate() function in pandas to interpolate missing values, and the SARIMAX() function in statsmodels to model missing values.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?