Frequency Mismatches

How to Align Time Series from Different Sources

Time series data often comes from various sources, each with its sampling frequency.?

For example, a market price feed might update hourly. At the same time, production data from a SCADA system might be reported every 15 minutes, and financial performance data might only be updated daily or weekly.?

This frequency mismatch can make it difficult to perform meaningful analysis or create accurate models, as misaligned data can lead to errors, gaps, or distorted insights.

Here’s how to tackle this common challenge and align time series data effectively:

1. Understand the Frequencies of Your Data

The first step is to assess the frequency at which each dataset updates:

• High frequency: Data recorded every second, minute, or hour, such as real-time power output or market price updates.
• Medium frequency: Daily or weekly data, such as weather forecasts or energy trades.
• Low frequency: Monthly, quarterly, or yearly data, such as financial reports or regulatory compliance metrics.

Knowing the frequencies helps determine the optimal approach for alignment.

2. Choose an Appropriate Target Frequency

When aligning data, you need to decide on a target frequency, this will depend on your analysis goals:

• If you’re analyzing short-term operational performance, a higher frequency (e.g., 15 minutes or hourly) is ideal to capture granular changes.
• For strategic planning or financial analysis, a lower frequency (e.g., daily or weekly) may suffice.

The target frequency serves as the baseline to which all other data will be resampled.

3. Resample the Data for Consistency

Once you’ve chosen a target frequency, use resampling techniques to align your datasets:

• Downsampling (Aggregating Data): When higher-frequency data needs to be reduced to a lower frequency (e.g., from 15-minute intervals to hourly), aggregation methods like sum, average, max, or min can be used.

Example: Converting 15-minute energy output to hourly totals by summing the intervals.

• Upsampling (Interpolating Data): When lower-frequency data needs to be increased to match a higher frequency, interpolation techniques can estimate values between recorded points.

Example: Interpolating daily weather data into hourly values using linear or spline interpolation.

Pro Tip: Always choose aggregation or interpolation methods based on the nature of the data. For example, energy output might use summation, while temperature might use interpolation.

4. Handle Missing Data Carefully

Frequency mismatches can create gaps in your data, especially when interpolating:

• Use forward-fill to propagate the last known value when dealing with steady-state variables like temperature.
• Use backward-fill for scenarios where future values are known (e.g., forecasts).
• For critical gaps, consider more advanced imputation methods like machine learning to estimate values accurately.

5. Align Timestamps with Precision

Even when frequencies are aligned, mismatched timestamps can cause issues:

• Ensure all timestamps are in the same timezone (preferably UTC) to avoid inconsistencies.
• Adjust for lag or lead effects in the data (e.g., delays in reporting production or market updates).

Pro Tip: Round timestamps to the nearest target interval (e.g., round 15:07 to 15:00 or 15:15) to simplify alignment.

6. Validate Your Aligned Data

Once data is aligned:

• Visualize it to ensure there are no unexpected gaps or anomalies.
• Cross-check aggregated totals or averages with the original datasets to confirm accuracy.
• Use domain knowledge to ensure the alignment makes sense, e.g., does interpolated weather data match observed patterns?

Why It Matters

Misaligned time series data can lead to flawed analysis, poor forecasting, and suboptimal decision-making.?

By carefully aligning data frequencies, you ensure that all datasets work seamlessly together, providing a more accurate picture for operational decisions, financial tracking, or strategic planning.

Whether you’re an analyst, portfolio manager, or operations lead, mastering frequency alignment is key to getting the most out of your time series data.