How to choose the right alpha value for EWMA in my Go project?

Use the formula alpha = 2/(N+1) to match the average age of an N-period sliding window; for example, a 60-second window with per-second samples requires alpha ~0.0328. The library's SimpleEWMA uses a constant alpha based on this, but VariableEWMA allows custom tuning via decay age parameters.

EWMA or simple moving average for real-time monitoring in Go?

EWMA is more memory-efficient and responsive to recent trends since it doesn't store full history, making it better for continuous streams. However, simple moving averages might be preferable if you need exact historical averages without exponential decay, but they require more memory for sample storage.

How to handle zero values in VividCortex's SimpleEWMA without causing jumps?

Avoid SimpleEWMA if zero is common; use VariableEWMA instead or pre-initialize the average with a non-zero value. The README notes SimpleEWMA treats zero as uninitialized, so consider adding a small epsilon or using a workaround like initializing with the first sample.

What's the memory overhead of VariableEWMA compared to SimpleEWMA?

VariableEWMA uses slightly over twice the memory of SimpleEWMA because it stores decay age and sample count for warm-up. SimpleEWMA has minimal footprint with constant decay, making it better for high-throughput scenarios where memory is a constraint.

Can I use VividCortex/ewma for data with variable time intervals?

No, the library assumes a fixed time interval of 1.0 between samples, so it won't support irregular timestamps. You'd need to resample your data or find an alternative EWMA implementation that incorporates time-based decay for accurate averaging.

How to properly warm up VariableEWMA when starting averaging?

VariableEWMA reports 0.0 until enough samples are added based on the decay age; add initial samples matching the desired window—e.g., for a decay age of 5, add at least a few samples—before relying on the average value to ensure it reflects the data accurately without skew.

Open-Awesome

ewma

MITGov1.2.0

A Go library implementing Exponentially Weighted Moving Average algorithms for efficient time-series data smoothing.

GitHub

452 stars38 forks0 contributors

What is ewma?

EWMA is a Go library that provides Exponentially Weighted Moving Average algorithms for continuous computation of averages that bias towards recent data points. It offers computationally inexpensive and memory-efficient implementations, making it suitable for real-time monitoring and time-series analysis where recent trends are more important than distant history.

Target Audience

Go developers building real-time monitoring systems, performance analysis tools, or time-series data processing applications where efficient, incremental averaging of streaming data is required.

Value Proposition

Developers choose EWMA for its specialized, performance-optimized implementations that balance low CPU/memory usage with flexibility, offering both simple constant-decay and configurable variable-decay algorithms through a consistent interface.

Overview

Exponentially Weighted Moving Average algorithms for Go.

Use Cases

Best For

Real-time monitoring of system metrics like CPU usage or request latency where recent data points are more indicative of current state.
Time-series analysis applications that require incremental averaging without storing full historical data.
Building lightweight performance dashboards that need efficient moving average calculations for streaming data.
Implementing adaptive thresholds in alerting systems that should respond quickly to recent trends.
Processing periodic data streams with fixed intervals where time-implicit decay simplifies integration.
Scenarios requiring trade-offs between memory efficiency (SimpleEWMA) and configurable decay with warm-up periods (VariableEWMA).

Not Ideal For

Applications with irregularly spaced time-series data, as the library assumes a fixed 1.0 interval between samples and lacks time-based decay support.
Scenarios where zero is a legitimate and frequent data point, due to SimpleEWMA's assumption that zero means uninitialized, causing abrupt average jumps.
Projects requiring active feature development or custom algorithm extensions, given the maintainers' policy of rejecting new feature pull requests and focusing only on minor fixes.

Pros & Cons

Pros

High Performance Implementation

SimpleEWMA is optimized for low CPU and memory consumption with constant decay, making it ideal for resource-constrained real-time monitoring, as highlighted in the README for efficient incremental averaging.

Flexible Averaging Control

VariableEWMA supports configurable decay ages with a warm-up period, allowing developers to fine-tune responsiveness versus stability for specific time windows, such as setting alpha based on desired average age.

Consistent API Design

Both SimpleEWMA and VariableEWMA conform to the MovingAverage interface, enabling easy swapping and testing of different strategies without code changes, simplifying integration into various Go applications.

Simplified Time Handling

The time-implicit decay model assumes fixed intervals between samples, reducing complexity for periodic data streams like system metrics, as noted in the README for straightforward use in monitoring tools.

Cons

Fixed Time Interval Limitation

The library cannot handle variable time intervals between samples, limiting its use for irregularly spaced data streams, a weakness explicitly admitted in the README with no current support for time-based decay.

Zero Value Handling Quirk

SimpleEWMA interprets a value of zero as uninitialized, leading to sharp average jumps when actual zeros occur in data, which can distort results in datasets where zero readings are common, as warned in the documentation.

Limited Development Scope

The project only accepts pull requests for minor fixes and rejects new features, which may hinder adoption for teams needing evolving or customized moving average algorithms, as stated in the contribution guidelines.

Frequently Asked Questions

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