SparkML预测PV

背景

公司需要根据过去一段时间内每天网站的流量数据，预测未来一段时间每日流量，这样，在流量高峰到来前，可以提前警示相关的运营、运维提前准备。

这是个典型的“时序预测问题”，关于时序预测的方法有很多，有规则法、机器学习、传统建模法等等。

本文主要讲述机器学习的方式。

由于工作中主要用的是Spark技术栈处理数据，所以这里也选用SparkML来解决。当然，机器学习的包和库又很多，完全可以用sklearn来做。实际上，数据分析阶段我用的是pandas、numpy、sklearn，效率更高些。

数据分析

初始数据很简单，只有两列：PV、日期

画个曲线图，观察一下：

从图中看出，发现2019-07前后整体差异很大，这其实是由于业务调整导致的。由于需求是预测未来几天的pv，那么一定是以现有的业务为基础，过早的数据反而是噪声，直接抛弃。

选取近半年的数据，再观察一下：

这个数据就相对比较稳定了。

整体观察，数据变化存在周期性，一个周期是一星期；工作日相对周末pv高些；

局部观察，节假日为高峰（但并非所有节假日都是高峰，同样这与具体业务相关，所以需要按自己的业务整理出节假日表）；

另外，非节假日也有高峰，可能的原因是有热点事件（2020年2月，疫情期间热点较多）；对于热点事件导致的流量高峰不可预测，所以我们尽量减小这类样本的影响，因此后边数据处理时会“去热点”。

模型选取

这里选取线性回归模型作为机器学习模型，并非是线性回归是最优的，而是趋势预测很容易想到线性回归模型，可以作为baseline，后续在此基础上尝试其他模型进行优化。

特征提取

1. 时间特征

经过上边的数据分析，可以知道周末、工作日、节假日对pv影响较大，因此可以把这几个值作为特征：

day_of_week // 星期几，取值1~7
is_weekend // 是否是周末，取值0、1，星期六和星期日是周末
is_holiday // 是否是节假日，取值0、1，节假日库根据实际业务维护

2. 均值特征

既然有周期性，那么
周一的pv与所有周一的平均值有一定关系
周二的pv与所有周二的平均值有一定关系
...
所以，每个day_of_week的平均值可以作为一个特征。

同样，周末、节假日都有类似的均值特征。

day_of_week_avg // 按 day_of_week 分组，求平均值
is_weekend_avg // 按 is_weekend 分组，取平均值
is_holiday_avg // 按 is_holiday 分组，取平均值

3. 中位数特征

与均值特征类似，可以有中位数特征

day_of_week_med // 按 day_of_week 分组，取中位数
is_weekend_med // 按 is_weekend 分组，取中位数
is_holiday_med // 按 is_holiday 分组，取中位数

4. 平移特征

均值特征、中位数特征反应的是整体的情况，实际上某日的pv很有可能取决于最近N天的pv。

具体N取几？需要多试试了。这里N取1到14，得到一组特征：

lag_1 // 平移1天，即昨天的pv
lag_2 // 平移2天，即两天前的pv
...
lag_7 // 平移7天，上周这天的pv
...
lag_14 // 平移14天，上上周这天的pv

平移后数据的样子：

+--------+----------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+
|      pv|       day|   lag_1|   lag_2|   lag_3|   lag_4|   lag_5|   lag_6|   lag_7|   lag_8|   lag_9|  lag_10|  lag_11|  lag_12|  lag_13|  lag_14|
+--------+----------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+
|15156440|2019-11-01|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|
|12633297|2019-11-02|15156440|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|
|11818845|2019-11-03|12633297|15156440|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|
|15130911|2019-11-04|11818845|12633297|15156440|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|
|14332734|2019-11-05|15130911|11818845|12633297|15156440|    null|    null|    null|    null|    null|    null|    null|    null|    null|    null|
|15972959|2019-11-06|14332734|15130911|11818845|12633297|15156440|    null|    null|    null|    null|    null|    null|    null|    null|    null|
|16366371|2019-11-07|15972959|14332734|15130911|11818845|12633297|15156440|    null|    null|    null|    null|    null|    null|    null|    null|
|16969708|2019-11-08|16366371|15972959|14332734|15130911|11818845|12633297|15156440|    null|    null|    null|    null|    null|    null|    null|
|12983425|2019-11-09|16969708|16366371|15972959|14332734|15130911|11818845|12633297|15156440|    null|    null|    null|    null|    null|    null|
|11759009|2019-11-10|12983425|16969708|16366371|15972959|14332734|15130911|11818845|12633297|15156440|    null|    null|    null|    null|    null|
|13700888|2019-11-11|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|12633297|15156440|    null|    null|    null|    null|
|15490684|2019-11-12|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|12633297|15156440|    null|    null|    null|
|15275479|2019-11-13|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|12633297|15156440|    null|    null|
|14978239|2019-11-14|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|12633297|15156440|    null|
|16900067|2019-11-15|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|12633297|15156440|
|15668745|2019-11-16|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|12633297|
|15102373|2019-11-17|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|
|16475787|2019-11-18|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|
|16946753|2019-11-19|16475787|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|
|17422016|2019-11-20|16946753|16475787|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|

特征处理代码：

        Dataset<Row> df = spark.read().schema(schema).option("header", "false").csv("file:///Users/sun/Downloads/pv_data.csv");
        df.createOrReplaceTempView("tmp");

        df = spark.sql("select * from tmp where day>='2019-11-01'");
        df.createOrReplaceTempView("tmp");

        // 补充待预测日期：
        int predDays = 7;
        String lastDay = spark.sql("select max(day) as day from tmp").first().getAs("day");
        Date lastDate = DateUtils.parseDate(lastDay, new String[]{"yyyy-MM-dd"});
        String sql = "select pv, day from tmp";
        for (int i=0; i<predDays; i++) {
            Date date = DateUtils.addDays(lastDate, (i + 1));
            String day = new SimpleDateFormat("yyyy-MM-dd").format(date);
            sql += " union (select 0, '" + day + "' from tmp limit 1)";
        }
        sql += " order by day asc";
        df = spark.sql(sql);
        df.createOrReplaceTempView("tmp");

        // 平移特征：
        int lagStart = 1;
        int lagEnd = 14;
        sql = "select *, ";
        for (int i=lagStart; i<=lagEnd; i++) {
            sql += "lag(pv, " + i + ") over (partition by null order by day) as lag_" + i;
            if (i <= lagEnd - 1)
                sql += ", ";
        }
        sql += " from tmp";
        df = spark.sql(sql);
        df.createOrReplaceTempView("tmp");

        // 时间特征：
        sql = "select *, " +
                "dayofweek(day) as day_of_week, " +
                "case when dayofweek(day)==1 or dayofweek(day)==7 then 1 else 0 end as is_weekend, " +
                "case when day in (" + Arrays.asList(holidays.split(",")).stream().map(s -> "'" + s + "'").collect(Collectors.joining(",")) + ") then 1 else 0 end as is_holiday " +
                "from tmp";
        df = spark.sql(sql);
        df.registerTempTable("tmp");

        // 均值特征：
        sql = "select tmp.*, t1.day_of_week_avg, t2.is_weekend_avg, t3.is_holiday_avg from tmp " +
                "left join (select day_of_week, avg(pv) as day_of_week_avg from tmp group by day_of_week) as t1 on tmp.day_of_week = t1.day_of_week " +
                "left join (select is_weekend, avg(pv) as is_weekend_avg from tmp group by is_weekend) as t2 on tmp.is_weekend = t2.is_weekend " +
                "left join (select is_holiday, avg(pv) as is_holiday_avg from tmp group by is_holiday) as t3 on tmp.is_holiday = t3.is_holiday ";
        df = spark.sql(sql);
        df.registerTempTable("tmp");

        // 中位数特征：
        sql = "select tmp.*, t1.day_of_week_med, t2.is_weekend_med, t3.is_holiday_med from tmp " +
                "left join (select day_of_week, percentile_approx(pv, 0.5) as day_of_week_med from tmp group by day_of_week) as t1 on tmp.day_of_week = t1.day_of_week " +
                "left join (select is_weekend, percentile_approx(pv, 0.5) as is_weekend_med from tmp group by is_weekend) as t2 on tmp.is_weekend = t2.is_weekend " +
                "left join (select is_holiday, percentile_approx(pv, 0.5) as is_holiday_med from tmp group by is_holiday) as t3 on tmp.is_holiday = t3.is_holiday ";
        df = spark.sql(sql);
        df.registerTempTable("tmp");

去热点（异常值处理）

之前提到，有些样本并非是节假日，但PV很高，可能是由于热点事件导致。

大致有两种情况：1. 运营搞了一些活动，刺激流量激增；2. 社会化热点事件（参考微博热搜）。

实际上，通过进一步的数据分析，可以知道主要原因是“疫情”间接带来的PV波动。

热点事件不像节假日一样有迹可循，而有一定的随机性、突发性。为了简化，我们采取一定策略，对异常值进行处理。

这里，使用策略为：如果非节假日PV高于中位数的1.5倍，那么取中位数。代码如下：

        // 异常值处理：
        // 非节假日，但流量超过中位数的1.5倍，认为这样的样本是异常的（可能是热点事件导致），处理为中位数
        df = spark.sql("select *, " +
                "if(is_holiday=0 and pv>day_of_week_med*1.5, day_of_week_med, pv) as y " +
                "from tmp order by day asc");
        df = df.na().drop();
        df.registerTempTable("tmp");

        // 平移特征0缺失值处理：处理为day_of_week_avg
        sql = "select *, ";
        for (int i=lagStart; i<=lagEnd; i++) {
            sql += "case when lag_" + i + ">0 then lag_"+i + " else day_of_week_avg end as lag_" + i + "_fix";
            if (i <= lagEnd - 1)
                sql += ", ";
        }
        sql += " from tmp";
        df = spark.sql(sql);
        df.registerTempTable("tmp");
        // 保存数据：
        df.write().option("header", "true").csv("file:///Users/sun/Downloads/df");

得到数据示例：

+--------+----------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+-----------+----------+----------+--------------------+--------------------+--------------------+---------------+--------------+--------------+--------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|      pv|       day|   lag_1|   lag_2|   lag_3|   lag_4|   lag_5|   lag_6|   lag_7|   lag_8|   lag_9|  lag_10|  lag_11|  lag_12|  lag_13|  lag_14|day_of_week|is_weekend|is_holiday|     day_of_week_avg|      is_weekend_avg|      is_holiday_avg|day_of_week_med|is_weekend_med|is_holiday_med|       y|           lag_1_fix|           lag_2_fix|           lag_3_fix|           lag_4_fix|           lag_5_fix|           lag_6_fix|  lag_7_fix|  lag_8_fix|  lag_9_fix| lag_10_fix| lag_11_fix| lag_12_fix| lag_13_fix| lag_14_fix|
+--------+----------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+-----------+----------+----------+--------------------+--------------------+--------------------+---------------+--------------+--------------+--------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|16900067|2019-11-15|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|12633297|15156440|          6|         0|         0|2.2269334259259257E7|2.1140308681818184E7| 2.047994621387283E7|       18144580|      17914823|      17128256|16900067|         1.4978239E7|         1.5275479E7|         1.5490684E7|         1.3700888E7|         1.1759009E7|         1.2983425E7|1.6969708E7|1.6366371E7|1.5972959E7|1.4332734E7|1.5130911E7|1.1818845E7|1.2633297E7| 1.515644E7|
|15668745|2019-11-16|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|12633297|          7|         1|         0|2.2007671444444444E7|2.1197627611111112E7| 2.047994621387283E7|       15728601|      15623119|      17128256|15668745|         1.6900067E7|         1.4978239E7|         1.5275479E7|         1.5490684E7|         1.3700888E7|         1.1759009E7|1.2983425E7|1.6969708E7|1.6366371E7|1.5972959E7|1.4332734E7|1.5130911E7|1.1818845E7|1.2633297E7|
|15102373|2019-11-17|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|          1|         1|         0|2.0387583777777776E7|2.1197627611111112E7| 2.047994621387283E7|       15245430|      15623119|      17128256|15102373|         1.5668745E7|         1.6900067E7|         1.4978239E7|         1.5275479E7|         1.5490684E7|         1.3700888E7|1.1759009E7|1.2983425E7|1.6969708E7|1.6366371E7|1.5972959E7|1.4332734E7|1.5130911E7|1.1818845E7|
|16475787|2019-11-18|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|          2|         0|         0|1.9976350222222224E7|2.1140308681818184E7| 2.047994621387283E7|       16614896|      17914823|      17128256|16475787|         1.5102373E7|         1.5668745E7|         1.6900067E7|         1.4978239E7|         1.5275479E7|         1.5490684E7|1.3700888E7|1.1759009E7|1.2983425E7|1.6969708E7|1.6366371E7|1.5972959E7|1.4332734E7|1.5130911E7|
|16946753|2019-11-19|16475787|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|          3|         0|         0|2.0061554769230768E7|2.1140308681818184E7| 2.047994621387283E7|       17121601|      17914823|      17128256|16946753|         1.6475787E7|         1.5102373E7|         1.5668745E7|         1.6900067E7|         1.4978239E7|         1.5275479E7|1.5490684E7|1.3700888E7|1.1759009E7|1.2983425E7|1.6969708E7|1.6366371E7|1.5972959E7|1.4332734E7|

模型训练

标准化

lag_、 _avg、 *_med 这些特征是pv，量级为千万级，对其进行标准化：

        // 标准化：lag_*、 *_avg、 *_med 特征进行标准化
        VectorAssembler vectorAssembler = new VectorAssembler()
                .setInputCols(new String[]{"lag_1", "lag_2", "lag_3", "lag_4", "lag_5", "lag_6", "lag_7", "lag_8", "lag_9", "lag_10", "lag_11", "lag_12", "lag_13", "lag_14", "day_of_week_avg", "is_weekend_avg", "is_holiday_avg", "day_of_week_med", "is_weekend_med", "is_holiday_med"})
                .setOutputCol("feature_vec");
        df = vectorAssembler.transform(df);
        MinMaxScaler scaler = new MinMaxScaler()
                .setInputCol("feature_vec")
                .setOutputCol("feature_out");
        df = scaler.fit(df).transform(df);

VectorAssembler 可以把 Dataset 的列转为Vector类型（后边算法API必须使用向量作为入参）；
MinMaxScaler 把特征缩放到[0,1]区间。

处理结果：

+--------+----------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+-----------+----------+----------+--------------------+--------------------+-------------------+---------------+--------------+--------------+--------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+--------------------+--------------------+
|      pv|       day|   lag_1|   lag_2|   lag_3|   lag_4|   lag_5|   lag_6|   lag_7|   lag_8|   lag_9|  lag_10|  lag_11|  lag_12|  lag_13|  lag_14|day_of_week|is_weekend|is_holiday|     day_of_week_avg|      is_weekend_avg|     is_holiday_avg|day_of_week_med|is_weekend_med|is_holiday_med|       y|  lag_1_fix|  lag_2_fix|  lag_3_fix|  lag_4_fix|  lag_5_fix|  lag_6_fix|  lag_7_fix|  lag_8_fix|  lag_9_fix| lag_10_fix| lag_11_fix| lag_12_fix| lag_13_fix| lag_14_fix|         feature_vec|            features|
+--------+----------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+-----------+----------+----------+--------------------+--------------------+-------------------+---------------+--------------+--------------+--------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+--------------------+--------------------+
|16900067|2019-11-15|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|12633297|15156440|          6|         0|         0|2.2269334259259257E7|2.1140308681818184E7|2.047994621387283E7|       18144580|      17914823|      17128256|16900067|1.4978239E7|1.5275479E7|1.5490684E7|1.3700888E7|1.1759009E7|1.2983425E7|1.6969708E7|1.6366371E7|1.5972959E7|1.4332734E7|1.5130911E7|1.1818845E7|1.2633297E7| 1.515644E7|[1.4978239E7,1.52...|[0.02878242285919...|
|15668745|2019-11-16|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|12633297|          7|         1|         0|2.2007671444444444E7|2.1197627611111112E7|2.047994621387283E7|       15728601|      15623119|      17128256|15668745|1.6900067E7|1.4978239E7|1.5275479E7|1.5490684E7|1.3700888E7|1.1759009E7|1.2983425E7|1.6969708E7|1.6366371E7|1.5972959E7|1.4332734E7|1.5130911E7|1.1818845E7|1.2633297E7|[1.6900067E7,1.49...|[0.05339190949495...|
|15102373|2019-11-17|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|11818845|          1|         1|         0|2.0387583777777776E7|2.1197627611111112E7|2.047994621387283E7|       15245430|      15623119|      17128256|15102373|1.5668745E7|1.6900067E7|1.4978239E7|1.5275479E7|1.5490684E7|1.3700888E7|1.1759009E7|1.2983425E7|1.6969708E7|1.6366371E7|1.5972959E7|1.4332734E7|1.5130911E7|1.1818845E7|[1.5668745E7,1.69...|[0.03762452432660...|
|16475787|2019-11-18|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|15130911|          2|         0|         0|1.9976350222222224E7|2.1140308681818184E7|2.047994621387283E7|       16614896|      17914823|      17128256|16475787|1.5102373E7|1.5668745E7|1.6900067E7|1.4978239E7|1.5275479E7|1.5490684E7|1.3700888E7|1.1759009E7|1.2983425E7|1.6969708E7|1.6366371E7|1.5972959E7|1.4332734E7|1.5130911E7|[1.5102373E7,1.56...|[0.03037198967476...|
|16946753|2019-11-19|16475787|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|14332734|          3|         0|         0|2.0061554769230768E7|2.1140308681818184E7|2.047994621387283E7|       17121601|      17914823|      17128256|16946753|1.6475787E7|1.5102373E7|1.5668745E7|1.6900067E7|1.4978239E7|1.5275479E7|1.5490684E7|1.3700888E7|1.1759009E7|1.2983425E7|1.6969708E7|1.6366371E7|1.5972959E7|1.4332734E7|[1.6475787E7,1.51...|[0.04795889832547...|
|17422016|2019-11-20|16946753|16475787|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|15972959|          4|         0|         0| 2.172384296153846E7|2.1140308681818184E7|2.047994621387283E7|       17928108|      17914823|      17128256|17422016|1.6946753E7|1.6475787E7|1.5102373E7|1.5668745E7|1.6900067E7|1.4978239E7|1.5275479E7|1.5490684E7|1.3700888E7|1.1759009E7|1.2983425E7|1.6969708E7|1.6366371E7|1.5972959E7|[1.6946753E7,1.64...|[0.05398973536338...|
|18010112|2019-11-21|17422016|16946753|16475787|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|16366371|          5|         0|         0|2.1671804769230768E7|2.1140308681818184E7|2.047994621387283E7|       17962984|      17914823|      17128256|18010112|1.7422016E7|1.6946753E7|1.6475787E7|1.5102373E7|1.5668745E7|1.6900067E7|1.4978239E7|1.5275479E7|1.5490684E7|1.3700888E7|1.1759009E7|1.2983425E7|1.6969708E7|1.6366371E7|[1.7422016E7,1.69...|[0.06007559655750...|
|17935725|2019-11-22|18010112|17422016|16946753|16475787|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|16969708|          6|         0|         0|2.2269334259259257E7|2.1140308681818184E7|2.047994621387283E7|       18144580|      17914823|      17128256|17935725|1.8010112E7|1.7422016E7|1.6946753E7|1.6475787E7|1.5102373E7|1.5668745E7|1.6900067E7|1.4978239E7|1.5275479E7|1.5490684E7|1.3700888E7|1.1759009E7|1.2983425E7|1.6969708E7|[1.8010112E7,1.74...|[0.06760631244495...|
|15623119|2019-11-23|17935725|18010112|17422016|16946753|16475787|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|12983425|          7|         1|         0|2.2007671444444444E7|2.1197627611111112E7|2.047994621387283E7|       15728601|      15623119|      17128256|15623119|1.7935725E7|1.8010112E7|1.7422016E7|1.6946753E7|1.6475787E7|1.5102373E7|1.5668745E7|1.6900067E7|1.4978239E7|1.5275479E7|1.5490684E7|1.3700888E7|1.1759009E7|1.2983425E7|[1.7935725E7,1.80...|[0.06665376836589...|
|14637174|2019-11-24|15623119|17935725|18010112|17422016|16946753|16475787|15102373|15668745|16900067|14978239|15275479|15490684|13700888|11759009|          1|         1|         0|2.0387583777777776E7|2.1197627611111112E7|2.047994621387283E7|       15245430|      15623119|      17128256|14637174|1.5623119E7|1.7935725E7|1.8010112E7|1.7422016E7|1.6946753E7|1.6475787E7|1.5102373E7|1.5668745E7|1.6900067E7|1.4978239E7|1.5275479E7|1.5490684E7|1.3700888E7|1.1759009E7|[1.5623119E7,1.79...|[0.03704027202242...|
+--------+----------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+-----------+----------+----------+--------------------+--------------------+-------------------+---------------+--------------+--------------+--------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+--------------------+--------------------+

features为处理后的特征列。

训练

        // 训练：使用 lastDay 之前的数据进行训练
        Dataset<Row> trainDataset = spark.sql("select day, features, pv, y from tmp where day<='" + lastDay + "' order by day asc");
        double maxR2 = 0.0D;
        double bestParam = 0.0D;
        LinearRegressionModel bestModel = null;
        // 搜索最优参数：
        for (int i=1; i<=10; i++) {
            LinearRegression lr = new LinearRegression()
                    .setLabelCol("y")
                    .setFeaturesCol("features")
                    .setMaxIter(10000)
                    .setRegParam(0.03) // 步长
                    .setElasticNetParam(0.1 * i);
            LinearRegressionModel model = lr.fit(trainDataset);
            LinearRegressionTrainingSummary trainingSummary = model.summary();
            System.out.println("RMSE: " + trainingSummary.rootMeanSquaredError());
            System.out.println("r2: " + trainingSummary.r2());
            if(trainingSummary.r2() > maxR2) {
                bestParam = 0.1 * i;
                maxR2 = trainingSummary.r2();
                bestModel = model;
            }
        }
        System.out.println("best param -> " + bestParam);
        System.out.println("best r2 -> " + maxR2);

这里使用LinearRegression ，主要调节setElasticNetParam参数值，详细参数说明可以参考文档。

从0.1~1.0，寻找一个最优值，使得模型r2最高，此时的模型作为最优模型。

最终，得到elasticnet为0.5时最优，r2为0.7008858790650143。

预测

对未来7天的数据进行预测

Dataset<Row> predDataset = spark.sql("select day, features, pv, y from tmp where day>'" + lastDay + "' order by day asc");
        bestModel.setPredictionCol("pv_pred");
        bestModel.transform(predDataset).show();

结果如下：

+----------+--------------------+---+---+--------------------+
|       day|            features| pv|  y|             pv_pred|
+----------+--------------------+---+---+--------------------+
|2020-04-28|[0.03159798985245...|  0|  0|1.7333708553490087E7|
|2020-04-29|[0.11516156320975...|  0|  0|1.7833363920196097E7|
|2020-04-30|[0.11449520118456...|  0|  0|1.7624262847742468E7|
|2020-05-01|[0.12214671526351...|  0|  0|3.6077728160918914E7|
|2020-05-02|[0.11879605768944...|  0|  0| 1.518647529881512E7|
|2020-05-03|[0.09805043124337...|  0|  0|1.5407320504048364E7|
|2020-05-04|[0.09278448304737...|  0|  0|  3.56043256732697E7|
+----------+--------------------+---+---+--------------------+

5月1日、5月4日是节假日，预计这两天将出现流量高峰。

引用

时序预测方法汇总
 SparkML文档

作者：易企秀工程师 Sun

SparkML预测PV

背景

数据分析

模型选取

特征提取

1. 时间特征

2. 均值特征

3. 中位数特征

4. 平移特征

去热点（异常值处理）

模型训练

标准化

训练

预测

引用

易企秀工程师

引用和评论

面试问你正则原理？你就跟他吹起来

手把手教你搭建DataEase二次开发环境(Windows)

Dolphinscheduler IDEA本地调试

数据资产管理实施路径盘点，一文读懂如何建设企业数据资产管理体系

Flink CDC：新一代实时数据集成框架

七猫如何用 StarRocks 打造用户增长新引擎?

饿了么基于Flink+Paimon+StarRocks的实时湖仓探索