time-series data and rust
Rust Crates for Time-Series Programming
Here’s a carefully selected list of high-quality Rust crates useful for time-series data processing, especially when dealing with stock prices, signals, or sequential datasets:
High-Quality Rust Crates for Time-Series Programming
1. polars
- Crate:
polars - Purpose: Fast DataFrame library; excellent for time-series processing (grouping, rolling windows, resampling).
- Key features:
- Rolling aggregations (
rolling_mean,rolling_sum). - GroupBy time features (resample by day, hour, etc.).
- Built-in datetime types.
- Rolling aggregations (
- View polars on crates.io
Critical Note: If you want "pandas-like" functionality for time-series, polars is the best Rust option right now.
2. chrono
- Crate:
chrono - Purpose: Dealing with date and time types precisely.
- Key features:
- Timestamps handling.
- Datetime parsing/formatting.
- Timezone support.
- View chrono on crates.io
Usage: Pair chrono with your data processing crates to manage time indices.
3. time-series
- Crate:
time-series - Purpose: Simple framework to build and work with time-series data.
- Key features:
- Time-series objects (
Series<T>) with timestamped values. - Interpolation, resampling, basic operations.
- Time-series objects (
- View time-series on crates.io
Critical Note: Not as mature as polars but lightweight and focused purely on time-series operations.
4. ta (Technical Analysis)
- Crate:
ta - Purpose: Technical analysis indicators for trading and stock price analysis.
- Key features:
- Moving Averages (SMA, EMA).
- RSI, MACD, Bollinger Bands, and more.
- View ta on crates.io
Usage: Essential for feature engineering in stock price prediction systems.
5. ndarray
- Crate:
ndarray - Purpose: N-dimensional array (tensor) library for efficient numerical processing.
- Key features:
- Efficient matrix and vector operations.
- Works well for large time-series numerical datasets.
- View ndarray on crates.io
Critical Note: Not time-series aware by itself, but perfect for raw numeric signal processing tasks.
Bonus Specialized Crates
| Crate | Purpose |
|---|---|
resample | Algorithms for signal resampling (for irregular time-series). |
interpolate | Interpolation utilities for filling missing time-series data points. |
statrs | Statistical functions (mean, variance, probability distributions). |
Recommended Stack for a Serious Time-Series Project
- polars — DataFrames and time-series indexing.
- chrono — Timestamp handling.
- ta — Feature engineering with technical indicators.
- ndarray — Pure numeric matrix computations if needed.
- serde — For CSV or JSON serialization/deserialization.
Critical Warning
Rust’s ecosystem for deep time-series modeling (like TensorFlow/Keras) is still emerging.
Crates like polars and ta are excellent for preprocessing.
For neural networks (especially LSTM), you may need to interoperate with Python frameworks like PyTorch or use FFI bindings.
Optional Next Step
Would you like a suggested Rust project template (Cargo.toml and project folder layout) for a serious time-series modeling application?