Question

我正在尝试使用Azure Stream Analytics计算一些线性回归。

设置：传感器向IoT-Hub发送温度和时间，Stream-Analytics正在使用SlidingWindow监听IoT-Hub，并应在此窗口中计算温度趋势（使用线性回归）。

事件如下所示：

{"deviceId":"sensor_1","temp":322.3376736446427,"time":1517500183940}

到目前为止我得到的SQL是：

WITH Step1 AS
(
select time AS x, avg(time) over () AS x_bar,
       temp AS y, avg(temp) over () AS y_bar
FROM inputStream
GROUP BY SlidingWindow(second,10) 
),
Step2 AS (
    SELECT (sum((x - x_bar) * (y - y_bar)) / sum((x - x_bar) * (x - x_bar))) AS slope,
           max(x_bar) AS x_bar_max,
           max(y_bar) AS y_bar_max    
    FROM Step1
),
FinalStep AS (
    SELECT  slope, 
            y_bar_max - x_bar_max * slope AS intercept
    FROM Step2
)

SELECT * INTO outputEventHub FROM FinalStep

来自here的原始线性回归SQL模板如下所示：

select slope, 
       y_bar_max - x_bar_max * slope as intercept 
from (
    select sum((x - x_bar) * (y - y_bar)) / sum((x - x_bar) * (x - x_bar)) as slope,
           max(x_bar) as x_bar_max,
           max(y_bar) as y_bar_max    
    from (
        select x, avg(x) over () as x_bar,
               y, avg(y) over () as y_bar
        from ols) s;
)

如果您在没有Stream Analytics的情况下知道更好的方法，我也很好。请提前分享您的想法和感谢！

Answer 1

ASA doc有两个变量回归的例子。 https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-high-frequency-trading

Azure流分析 - 计算线性回归

1 个答案: