Combining Multiple Time Series Data for Effective Analysis

Working with multiple time series data can be both challenging and rewarding, especially when you aim to derive meaningful insights through analysis. The choice of how to combine these data series depends on the nature of the data and the intended analysis. In this article, we will explore various methods to effectively merge multiple time series data, including concatenation, merging, resampling, alignment, and weighted combination.

Concatenation

Concatenation involves merging time series data from the same variable or measurement but captured during different time periods or from different sources. This method is straightforward and useful when you simply want to pool data for a longer time horizon.

Example:

import pandas as pd
# Example time series data
ts1  ([1, 2, 3], index_range('2023-01-01', periods3))
ts2  ([4, 5, 6], index_range('2023-01-04', periods3))
# Concatenating
combined_ts  ([ts1, ts2], ignore_indexFalse)

The ignore_indexFalse parameter ensures that the original indices are retained, preserving the temporal order of the data.

Merging

Merging is a technique used when the time series data share a common key or index. This is particularly useful when you have multiple variables measured at the same time or at regular intervals, and you want to analyze them together.

Example:

df1  ({'value1': [1, 2, 3]}, index_range('2023-01-01', periods3))
df2  ({'value2': [4, 5, 6]}, index_range('2023-01-01', periods3))
# Merging dataframes with common index
merged_df  (df1, df2, how'outer', left_indexTrue, right_indexTrue)

The how'outer' parameter is used to perform an outer merge, which includes all records from both data frames, filling missing values with NaNs.

Resampling

Resampling is necessary when the time series data are recorded at different frequencies. Resampling allows you to standardize the frequency of the data, making it easier to combine them.

Example:

ts_daily  ([1, 2, 3], index_range('2023-01-01', periods3, freq'D'))
ts_monthly  ([10, 20], index_range('2023-01-31', periods2, freq'M'))
types2_resampled  ts_('M').sum()
combined_resampled  ([types2_resampled, ts_monthly], axis0)

The resample('M').sum() method is used to resample the daily data to a monthly basis, summing values within each month.

Aligning

Alignment is crucial when the time series have different start dates or lengths. Aligning them ensures that the data points align based on their indices, facilitating a more accurate analysis.

Example:

ts1  ([1, 2, 3], index_range('2023-01-01', periods3))
ts2  ([4, 5], index_range('2023-01-02', periods2))
aligned_df  ({'ts1': ts1, 'ts2': ts2}).fillna(0)

The use of fillna(0) fills in any missing values with zeros, ensuring all time series have the same length.

Weighted Combination

Weighted Combination allows you to combine time series data based on predefined weights. This method is useful when you want to give different levels of importance to each series.

Example:

weights  [0.6, 0.4]
combined_weighted  (ts1 * weights[0]   ts2 * weights[1]).dropna()

This code multiplies each series by the corresponding weight and then sums them, ensuring that the final result is a single series with no missing values.

Conclusion

The choice of method for combining multiple time series data depends on the specifics of your data and the analysis goals. Always ensure that the time series are appropriately indexed and aligned to avoid misinterpretation of the results. By carefully selecting the appropriate method, you can enhance the accuracy and relevance of your analysis.