Module time_series

aprender

Module time_series 

Source
Expand description

Time series analysis and forecasting.

This module provides algorithms for analyzing and forecasting time series data:

  • ARIMA (Auto-Regressive Integrated Moving Average)
  • Differencing for stationarity
  • Basic forecasting metrics

§Design Principles

Following the Toyota Way and aprender’s quality standards:

  • Zero unwrap() calls (Cloudflare-class safety)
  • Result-based error handling with AprenderError
  • Comprehensive test coverage (≥95%)
  • Pure Rust implementation

§Quick Start

use aprender::time_series::ARIMA;
use aprender::primitives::Vector;

// Time series data (e.g., monthly sales)
let data = Vector::from_slice(&[10.0, 12.0, 13.0, 15.0, 14.0, 16.0, 18.0, 17.0]);

// Create ARIMA(1, 1, 1) model
let mut model = ARIMA::new(1, 1, 1);

// Fit model to data
model.fit(&data).expect("fit should succeed");

// Forecast next 3 periods
let forecast = model.forecast(3).expect("forecast should succeed");
println!("Forecasted values: {:?}", forecast);

§References

  • Box, G. E. P., & Jenkins, G. M. (1976). “Time Series Analysis: Forecasting and Control.”
  • Hyndman, R. J., & Athanasopoulos, G. (2018). “Forecasting: Principles and Practice.”

Structs§

ARIMA
ARIMA (Auto-Regressive Integrated Moving Average) model.