Oracle/SQLでの時系列データの予測

REGR 線形回帰関数。

--Ordinary least squares forecast for each customer for the next year.
select
    cust_id,
    max(year) +1 forecast_year,
    -- y = mx+b
    regr_slope(revenue, year)
        * (max(year) + 1)
        + regr_intercept(revenue, year) forecasted_revenue
from customer_data
group by cust_id;

CUST_ID   FORECAST_YEAR   FORECASTED_REVENUE
-------   -------------   ------------------
1                  2018               730868
2                  2018                50148
4                  2018                 7483
3                  2018                -9920

以下はサンプルスキーマです。または、このSQLFiddle を使用できます。 。

create table customer_data
(
    cust_id number,
    year number,
    revenue number
);

insert into customer_data
select 1, 2016, 679862 from dual union all
select 1, 2017, 705365 from dual union all
select 2, 2016, 51074  from dual union all
select 2, 2017, 50611  from dual union all
select 3, 2016, 190706 from dual union all
select 3, 2017, 90393  from dual union all
select 4, 2016, 31649  from dual union all
select 4, 2017, 19566  from dual;

REGR 関数は数値ペアを処理しますが、「収益を0未満にすることはできません」などのビジネスルールを理解していません。予測を常に0以上に保つように制限する場合は、CASE 式が役立つ場合があります：

--Forecasted revenue, with minimum forecast of 0.
select cust_id, forecast_year,
    case when forecasted_revenue < 0 then 0 else forecasted_revenue end forecasted_revenue
from
(
    --Ordinary least squares forecast for each customer for the next year.
    select
        cust_id,
        max(year) +1 forecast_year,
        -- y = mx+b
        regr_slope(revenue, year)
            * (max(year) + 1)
            + regr_intercept(revenue, year) forecasted_revenue
    from customer_data
    group by cust_id
);

CUST_ID   FORECAST_YEAR   FORECASTED_REVENUE
-------   -------------   ------------------
1                  2018               730868
2                  2018                50148
4                  2018                 7483
3                  2018                    0