How can you handle time series data with missing values?

Lors d’un projet industriel de classification de série temporelle, j’ai été confronté à des manques de données des séries temporelles en exploitation. Ces manques, dus à l’évolution de la charge calculatoire de l’automate industriel, nous ont imposé de trouver une solution pour compléter les séries temporelles lorsqu’elles sont incomplètes. Il existe bon nombre de méthodes, la première que nous avons retenue la méthode d’interpolation, la méthode s’est avéré efficace, mais elle atteint ses limites lorsque des séquences trop grandes sont manquantes. Le choix définitif, c’est porté sur les réseaux de neurones et l’architecture LSTM pour générer les points manquants à partir de l’existant et ainsi être plus précis qu’avec l’interpolation.

In light of so many methods that help interpolating time series missing data, we often miss out on what could be a very interesting process of investigating why data is missing in the first place. While there's more incentive for this kind of investigation in the academic realm, doing so at the business level can also be a worthwhile use of resources as it can give you an insight on what to avoid when moving through a project that relies on time series data.

Identifying the cause of missing values in time series data helps improve data quality, resolve problems, select appropriate imputation methods, assess the impact, ensure data governance, implement monitoring, allocate resources, enhance system performance, and gain valuable insights from the data. This understanding guides data engineering processes and contributes to reliable and valuable data management.

The first step in handling missing values is identifying their cause. Are they random, systematic, or intentional? Random missing values are due to errors or outliers during data collection. Systematic missing values stem from patterns or seasonality in data generation. Intentional missing values result from data processing activities like censorship or aggregation. Understanding the cause guides the approach to handling missing values.

For handling missing values in time series data, consider methods like interpolation, imputation, or model-based approaches. Interpolation fills gaps linearly or using time-based trends. Imputation replaces missing values with statistical measures like mean or median. Model-based methods use algorithms to predict missing values based on observed data patterns. The choice depends on data nature and analysis goals.

Selecting the appropriate method for handling missing values in time series data involves understanding data patterns, identifying the cause, considering domain knowledge, evaluating the data impact, and aligning with data objectives. It also requires knowledge of imputation techniques, assessing data size and time sensitivity, recognizing biases, validation, sensitivity analysis, documentation, and consultation. Making a well-informed choice is essential for reliable data analysis and modeling.

The second step in handling missing values is selecting an appropriate method. Three main methods exist: deletion, imputation, and modeling. Deletion involves removing missing values or entire rows/columns containing them, reducing data size and complexity but potentially introducing bias and information loss. Imputation replaces missing values with estimates based on available data, preserving data structure and completeness but potentially introducing noise and uncertainty. Modeling uses statistical or machine learning techniques to account for missing values, improving data accuracy and robustness but requiring more computational resources and assumptions.

There are several quality models from the past and present that can help you avoid deleting or imputing data. To list a few: - ARIMA (AutoRegressive Integrated Moving Average) - State space models and Kalman filtering - Random forests - Gradient boosting machines (GBM) - LSTM (Long Short-Term Memory) - GRU (Gated Recurrent Units) - Prophet

For longer periods of missing data, I applied a more sophisticated approach using time series forecasting models like ARIMA. This allowed me to predict the missing values based on the patterns observed in the data. The blend of these techniques helped maintain the integrity of the time series analysis, leading to more accurate and reliable insights from the climate data.

When faced with extended gaps in the data, I employed advanced time series forecasting methods, such as ARIMA, to estimate the missing values. These predictions were based on the trends and patterns identified within the data. This combination of strategies was crucial in preserving the coherence of the time series analysis, resulting in enhanced accuracy and dependability of the insights derived from the climate data.

As an expert data engineer, using data engineering tools is crucial for enhancing efficiency, scalability, data integration, transformation, storage, security, monitoring, governance, collaboration, and more. These tools provide the infrastructure needed for reliable data management and analytics, allowing organizations to make informed decisions and effectively leverage their data assets. The choice of tools should be aligned with an organization's unique data requirements and objectives.

The third step involves using tools to implement your chosen method. Depending on your platform and language, different tools are available for handling missing time series data. In Python, pandas can be used to delete or impute missing values using methods like mean, median, mode, linear interpolation, or forward/backward filling. Statsmodels or sklearn can model missing values using techniques like regression, clustering, or time series decomposition. In SQL, functions like IS NULL, COALESCE, or LAG/LEAD can delete or impute missing values with simple rules/logic. Window functions or analytical functions can model missing values with aggregate/partitioned calculations.

If you opt to use a model to infer and insert missing data into your dataset, statsmodels offers the best toolset for this process when using traditional time series models. Whereas for machine learning methods, you can with sklearn and/or TensorFlow/PyTorch. Meanwhile Prophet, a Facebook/Meta product, has its own Python package.

The fourth step involves evaluating the impact of handling missing values on data quality and analysis results. Metrics like mean squared error, correlation coefficient, or R-squared can measure how well your method preserves or improves original data As a data engineer, I believe that handling missing values is a critical aspect of data preprocessing. It's important to understand the nature of the missingness and apply appropriate techniques to handle it. Deletion, imputation, and modeling each have their pros and cons, and the choice of method should be guided by the specific context and requirements of the analysis.

Evaluating the impact of missing data is crucial for ensuring data quality, model performance, minimizing bias, resource allocation, informed decision-making, risk assessment, reporting accuracy, data strategy optimization, transparency, resource efficiency, data governance, and overall data reliability and value. It guides organizations in making informed decisions and optimizing data handling processes.

When using metrics, avoid the mistake of only looking at whether it got up and down. A smaller mean squared error doesn't necessarily mean your dataset is now better. In a proper data evaluation process, nothing can be seen in isolation.

Documenting the process doesn't need to be a tedious process of filling in a Word document listing everything that has been done. It can often be a matter of adding comments to the code you wrote in any of the tools you utilised in this process, explaining in simple terms why a certain step was taken.

Documenting the data engineering process helps in maintaining transparency, knowledge sharing, error tracking, process improvement, and compliance with data governance. It provides a structured reference for understanding data workflows and facilitates efficient collaboration among team members.

Another approach I used in a different project involved machine learning. The project was focused on financial time series data, which had missing values due to market closure days and system outages. To tackle this, I utilized a Random Forest regression model, trained on the available data to predict missing values. This approach considered various features like historical trends, volatility patterns, and market cycles, offering a more nuanced prediction than simple interpolation. The model's ability to handle non-linear relationships in the data proved particularly useful in forecasting missing values in a complex financial series. This overall analysis, allowing for more robust investment strategies and risk assessments.