chronos-ts 0.1.0

High-performance, parallelized time series forecasting and auto-ARIMA engine in Rust.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149

// src/viz.rs

use crate::decomposition::ProphetPrediction;
use crate::errors::{ChronosError, Result};
use chrono::NaiveDate;
use serde::{Deserialize, Serialize};
use std::fs::File;
use std::io::Write;
use std::path::Path;

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChartDataPoint {
    pub date: String,
    pub yhat: f64,
    pub yhat_lower: Option<f64>,
    pub yhat_upper: Option<f64>,
    pub trend: f64,
    pub seasonal: f64,
    pub holidays: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DecompositionExport {
    pub series: Vec<ChartDataPoint>,
}

pub struct VisualizationExporter;

impl VisualizationExporter {
    pub fn to_export_data(
        dates: &[NaiveDate],
        pred: &ProphetPrediction,
    ) -> Result<DecompositionExport> {
        let n = dates.len();
        if pred.yhat.len() != n {
            return Err(ChronosError::InvalidParameters(
                "Dates length does not match prediction length".into(),
            ));
        }

        let mut series = Vec::with_capacity(n);

        for i in 0..n {
            let yhat_lower = pred.yhat_lower.as_ref().map(|v| v[i]);
            let yhat_upper = pred.yhat_upper.as_ref().map(|v| v[i]);

            series.push(ChartDataPoint {
                date: dates[i].to_string(),
                yhat: pred.yhat[i],
                yhat_lower,
                yhat_upper,
                trend: pred.trend[i],
                seasonal: pred.seasonal[i],
                holidays: pred.holidays[i],
            });
        }

        Ok(DecompositionExport { series })
    }

    pub fn to_json(dates: &[NaiveDate], pred: &ProphetPrediction) -> Result<String> {
        let export_data = Self::to_export_data(dates, pred)?;
        Ok(serde_json::to_string_pretty(&export_data)?)
    }

    pub fn to_html_string(
        dates: &[NaiveDate],
        pred: &ProphetPrediction,
        title: &str,
    ) -> Result<String> {
        let json_data = Self::to_json(dates, pred)?;

        let html = format!(
            r#"<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>{title}</title>
    <script src="https://cdn.plot.ly/plotly-2.32.0.min.js"></script>
    <style>
        body {{ font-family: sans-serif; margin: 0; padding: 24px; background: #0f172a; color: #f8fafc; }}
        .container {{ max-width: 1200px; margin: 0 auto; }}
        .chart-card {{ background: #1e293b; border-radius: 8px; padding: 16px; margin-bottom: 24px; }}
        #forecast-chart, #components-chart {{ width: 100%; height: 450px; }}
    </style>
</head>
<body>
    <div class="container">
        <h1>{title}</h1>
        <div class="chart-card"><div id="forecast-chart"></div></div>
        <div class="chart-card"><div id="components-chart"></div></div>
    </div>
    <script>
        const payload = {json_data};
        const dates = payload.series.map(d => d.date);
        const yhat = payload.series.map(d => d.yhat);
        const trend = payload.series.map(d => d.trend);
        const seasonal = payload.series.map(d => d.seasonal);
        const holidays = payload.series.map(d => d.holidays);

        const hasUpper = payload.series[0] && payload.series[0].yhat_upper !== null;
        const forecastTraces = [];

        if (hasUpper) {{
            const yhatUpper = payload.series.map(d => d.yhat_upper);
            const yhatLower = payload.series.map(d => d.yhat_lower);
            forecastTraces.push({{
                x: dates.concat(dates.slice().reverse()),
                y: yhatUpper.concat(yhatLower.slice().reverse()),
                fill: 'tozerox',
                fillcolor: 'rgba(59, 130, 246, 0.2)',
                line: {{ color: 'transparent' }},
                name: 'Uncertainty Interval',
                type: 'scatter'
            }});
        }}

        forecastTraces.push({{ x: dates, y: yhat, mode: 'lines', name: 'Forecast (yhat)', line: {{ color: '#3b82f6', width: 2.5 }} }});
        Plotly.newPlot('forecast-chart', forecastTraces, {{ title: 'Forecast', paper_bgcolor: '#1e293b', plot_bgcolor: '#1e293b', font: {{ color: '#f8fafc' }} }});

        const componentTraces = [
            {{ x: dates, y: trend, mode: 'lines', name: 'Trend', line: {{ color: '#10b981' }} }},
            {{ x: dates, y: seasonal, mode: 'lines', name: 'Seasonality', line: {{ color: '#f59e0b' }} }},
            {{ x: dates, y: holidays, mode: 'lines', name: 'Holidays', line: {{ color: '#ec4899' }} }}
        ];
        Plotly.newPlot('components-chart', componentTraces, {{ title: 'Components', paper_bgcolor: '#1e293b', plot_bgcolor: '#1e293b', font: {{ color: '#f8fafc' }} }});
    </script>
</body>
</html>"#,
            title = title,
            json_data = json_data
        );

        Ok(html)
    }

    pub fn save_html<P: AsRef<Path>>(
        dates: &[NaiveDate],
        pred: &ProphetPrediction,
        title: &str,
        path: P,
    ) -> Result<()> {
        let html_content = Self::to_html_string(dates, pred, title)?;
        let mut file = File::create(path)?;
        file.write_all(html_content.as_bytes())?;
        Ok(())
    }
}