Downsample Time-Series (LTTB)
Shrink thousands of points to a chartable few with Largest-Triangle-Three-Buckets, min/max, M4, or every-nth — original rows kept verbatim. Runs entirely in your browser, no upload.
About this tool
Charts choke on raw time-series data: plotting 100,000 samples into an 800-pixel-wide graph wastes bandwidth and rendering time without adding any visible detail. This tool reduces a large series to a target number of points while keeping the curve's visual shape — peaks, dips, and trends stay where they are. Paste CSV (from a spreadsheet, a sensor log, or a database export) or a JSON array, choose how many points you want, and copy the result.
The default algorithm is LTTB (Largest-Triangle-Three-Buckets): it splits the series into equal buckets and keeps, from each bucket, the point that forms the largest triangle with its neighbours — the point that contributes most to the line's shape. The first and last points are always kept. Three alternatives cover other needs: Min/Max keeps the minimum and maximum of each bucket (best when you must never miss a spike), M4 keeps first/min/max/last per bucket (the classic error-free line-rendering aggregation), and Every n-th takes a plain uniform stride.
All four algorithms select original points — nothing is averaged or interpolated. That means CSV rows come back verbatim: every column, the exact original formatting, and the header row are preserved. JSON arrays come back as the selected original elements.
Worked example
Input — 20 readings with two spikes (45 at t=5, 80 at t=17), downsampled to 8 points with LTTB:
t,v
1,10
2,12
3,11
4,13
5,45
6,12
7,10
8,11
9,13
10,12
11,14
12,13
13,12
14,15
15,13
16,12
17,80
18,13
19,12
20,11
Output — both spikes and both endpoints survive:
t,v
1,10
4,13
5,45
8,11
11,14
16,12
17,80
20,11
With output = indices the same run returns [0,3,4,7,10,15,16,19] — the
0-based data-row numbers that were kept, handy for selecting rows
programmatically from the original array.
Input formats
- CSV — comma, tab, or semicolon delimited (auto-detected). Two or more
columns: the first is x (time), the second is y, unless you set
x_column/y_column(header name or 1-based number). One column: values only, row order is the time axis. A non-numeric first row is treated as a header and kept. - JSON —
[10, 12, 45](values only),[[x, y], …]pairs, or[{"time": …, "value": …}, …]objects (x/y keys auto-detected fromx,t,time,timestamp,ts,dateandy,v,value,val). - Timestamps — x values may be numbers (epoch, sample number) or ISO-8601
dates/times (
2024-01-05,2024-01-05T12:30:00Z).
Limits and edge cases
- Input is capped at 2,000,000 bytes (~2 MB); the target point count at 100,000. A series already at or below the target is returned unchanged.
- x values must be sorted ascending (equal values are fine) — the tool
reports the first out-of-order row instead of guessing. Set
x_columntoindexto ignore a column and use row order. - Non-numeric or NaN y values are an error naming the offending line — fix or remove those rows first.
- Quoted CSV fields may not contain line breaks; decimal commas (
1,5) are not supported — use dot decimals. - M4 needs at least 4 target points; Min/Max and M4 round the target down to whole buckets, so they can return slightly fewer points than requested.
FAQ
What is LTTB and why is it the default?
Largest-Triangle-Three-Buckets (from Sveinn Steinarsson's 2013 thesis) divides
the series into points − 2 equal buckets and keeps, per bucket, the point
forming the largest triangle with the previously kept point and the next
bucket's average. It is the standard choice for visual downsampling because
the reduced line is hard to distinguish from the original at chart resolution,
it runs in a single O(n) pass, and it always keeps the first and last point.
This implementation matches the canonical reference implementation
bucket-for-bucket.
Which algorithm should I pick?
Use LTTB for charts — it gives the best overall shape at a given point budget. Use Min/Max when missing a single outlier spike would be unacceptable (alerting dashboards, sensor anomaly review): it keeps every bucket's extremes. M4 additionally keeps each bucket's first and last point, which makes pixel-perfect line rendering possible when buckets align with pixel columns. Every n-th is a plain uniform stride — predictable spacing, but it can skip over narrow spikes entirely.
Does downsampling change my values?
No. Every algorithm here selects a subset of your original points — nothing is averaged, smoothed, or interpolated. CSV rows are emitted verbatim with all their columns and the header; JSON output contains the original elements. If you need per-bucket averages instead, that is aggregation (resampling), a different operation from shape-preserving downsampling.
Can I get the row numbers instead of the data?
Yes — set the output to indices to get a JSON array of the 0-based data row numbers that were kept (the header does not count). Use it to slice the original dataset in your own code, the same way library implementations return index arrays.
My CSV has many columns — which ones are used?
By default the first column is x (time) and the second is y. Point the tool at
other columns with x_column / y_column, using either the header name
(case-insensitive) or a 1-based column number. Downsampling is driven by that
one y series, but kept rows are returned whole, with every column intact.
Developer & Automation Access
Run it from the terminal
Same engine as this page, headless — via the gizza CLI:
gizza tool downsample-timeseries "t,v
1,10
2,12
3,45
4,11
5,13
6,12"New to the CLI? Get gizza →
Open it by URL
Pre-fill and auto-run this tool with query parameters — the names match the API/CLI:
https://gizza.ai/tools/downsample-timeseries/?data=t%2Cv%0A1%2C10%0A2%2C12%0A3%2C45%0A4%2C11%0A5%2C13%0A6%2C12&points=100&algorithm=lttb&x_column=blank%20%3D%20first%20column&y_column=blank%20%3D%20second%20column&header=true&output=pointsMachine-readable descriptor: tool.json — title + parameters JSON Schema for agents.