Is Aggregate Two Time Series easy or hard?

Aggregate Two Time Series is generally considered a medium-level problem. The core logic is straightforward, but choosing the right strategy based on sorted versus unsorted input is what interviewers evaluate. Candidates are expected to optimize beyond brute force.

Aggregate Two Time Series Python/Java solution

Python solutions typically use dictionaries or collections.defaultdict for aggregation. Java implementations commonly use HashMap or TreeMap when sorted output is required. Both languages support an O(n + m) implementation.

How to solve Aggregate Two Time Series in O(n)?

Use a hash map or two pointers depending on the input format. With a hash map, iterate through both series once and accumulate values by timestamp. If the series are sorted, two pointers merge them in a single pass while aggregating matching timestamps.

What is the best approach for Aggregate Two Time Series?

The best approach depends on whether the input series are sorted. For unsorted data, a hash map aggregation solution runs in O(n + m) time and handles duplicate timestamps efficiently. If both series are sorted by timestamp, a two-pointer merge approach achieves the same linear complexity with lower extra memory usage.

Is Aggregate Two Time Series asked at Google/Amazon/Meta?

Time series aggregation and merge-style problems appear frequently in backend and data-processing interview rounds at companies like Google, Amazon, and Meta. Interviewers often test hash maps, sorting, and linear merge strategies through variations of this problem.

What data structure is used in Aggregate Two Time Series?

The most common data structure is a hash map keyed by timestamp. It allows constant average-time insertion and lookup while aggregating values. Sorted input variants often use arrays with the two-pointer technique instead of extra storage.

What is the time complexity of Aggregate Two Time Series?

The optimal solution runs in O(n + m) time, where n and m are the sizes of the two time series. Hash map aggregation and two-pointer merging both process each entry once. A brute force comparison approach takes O(n * m) time and is not suitable for large datasets.

Description Hints 3 Solution Video 9:10

4001. Aggregate Two Time Series

Medium57.9% acceptance

You are given two 2D integer arrays series1 and series2.

Each element in both series is of the form [timestamp, value], where:

timestamp is an integer representing the time.
value is an integer representing the value at that timestamp.

Each array is sorted in strictly increasing order of timestamp.

For any timestamp not present in a series, its value is taken from the next available timestamp in the same series if one exists. Otherwise, its value is considered 0.

The aggregated series is formed by summing the corresponding values from both series at every timestamp that appears in either series.

Return the aggregated series as a 2D integer array of [timestamp, summedValue] pairs, sorted in strictly increasing order of timestamp.

Example 1:

Input: series1 = [[1,3],[4,1]], series2 = [[2,2],[5,2]]

Output: [[1,5],[2,3],[4,3],[5,2]]

Explanation:

Timestamp	`series1`	`series2`	`summedValue`
1	3	2	5
2	1	2	3
4	1	2	3
5	0	2	2

Thus, the aggregated series is [[1, 5], [2, 3], [4, 3], [5, 2]].

Example 2:

Input: series1 = [[1,5],[3,1]], series2 = [[2,2]]

Output: [[1,7],[2,3],[3,1]]

Explanation:

Timestamp	`series1`	`series2`	`summedValue`
1	5	2	7
2	1	2	3
3	1	0	1

Thus, the aggregated series is [[1, 7], [2, 3], [3, 1]].

Example 3:

Input: series1 = [[1,5]], series2 = [[1000000000,2]]

Output: [[1,7],[1000000000,2]]

Explanation:

At timestamp 1, the next available value in series2 is 2 at timestamp 1000000000. At timestamp 1000000000, there is no later timestamp in series1, so its value is 0. Only timestamps that appear in at least one of the two series are included.

Constraints:

1 <= series1.length, series2.length <= 10⁵
series1[i].length == series2[i].length == 2
1 <= series1[i][0], series2[i][0] <= 10⁹
1 <= series1[i][1], series2[i][1] <= 10⁹
Each series is sorted in strictly increasing order of timestamp.

Topics

Array Two Pointers

Hints open one at a time

Hint 1

Merge the timestamps from both series in increasing order, similarly to the merge step of merge sort.

Hint 2

Because a missing timestamp uses the next available value, process the merged timestamps from right to left while maintaining the next available value in each series.

Hint 3

At each timestamp, update the maintained value for every series containing that timestamp, then add the two maintained values to the answer.

9:10

Still stuck?Read the written solution, or watch a walkthrough.

Open solution

LanguageLoading editor…

1class Solution:
2    def aggregateTimeSeries(self, series1: list[list[int]], series2: list[list[int]]) -> list[list[int]]:
3        

Starter codePython 3Spaces: 4

Examples

Case 1Case 2Case 3

Input

[[1,3],[4,1]]
[[2,2],[5,2]]

Input

[[1,5],[3,1]]
[[2,2]]

Input

[[1,5]]
[[1000000000,2]]

Your code is saved on this device.Solution