Oracle database - Analytic functions - Advanced cases

Analytic Functions
Advanced Cases
Kim Berg Hansen
T. Hansen Gruppen A/S

Picking by FIFO
 Sales forecasting
Cases

 FIFO – First-In-First-Out principle
● Pick oldest items first
 Picking route
● Don’t drive back and forth through the aisles of the
warehouse
 Single SQL
● Utilize the power of the database
Case: Picking by FIFO

create table inventory (
item varchar2(10) -- identification of the item
, loc varchar2(10) -- identification of the location
, qty number -- quantity present at that location
, purch date -- date that quantity was purchased
);
insert into inventory values('Ale' , '1-A-20', 18, DATE '2014-02-01');
insert into inventory values('Ale' , '1-C-05', 18, DATE '2014-02-03');
insert into inventory values('Ale' , '2-D-07', 9, DATE '2014-02-04');
insert into inventory values('Bock', '1-A-02', 18, DATE '2014-02-06');
insert into inventory values('Bock', '1-B-11', 4, DATE '2014-02-05');
insert into inventory values('Bock', '1-C-04', 12, DATE '2014-02-03');
insert into inventory values('Bock', '1-B-15', 2, DATE '2014-02-02');
insert into inventory values('Bock', '2-D-23', 1, DATE '2014-02-04');
Inventory
loc = 1-A-20
1 = warehouse
A = aisle
20 = position

create table orderline (
ordno number -- id-number of the order
, item varchar2(10) -- identification of the item
, qty number -- quantity ordered
);
insert into orderline values (42, 'Ale' , 24);
insert into orderline values (42, 'Bock', 18);
Order
One order
24 Ale
18 Bock

Join up
select o.item
, o.qty ord_qty
, i.loc
, i.purch
, i.qty loc_qty
from orderline o
join inventory i
on i.item = o.item
where o.ordno = 42
order by o.item, i.purch, i.loc;
ITEM ORD_QTY LOC PURCH LOC_QTY
----- ------- ------- ---------- -------
Ale 24 1-A-20 2014-02-01 18
Ale 24 2-A-02 2014-02-02 24
Ale 24 1-C-05 2014-02-03 18
Ale 24 2-D-07 2014-02-04 9
Ale 24 1-A-31 2014-02-05 12
Bock 18 1-B-15 2014-02-02 2
Bock 18 1-C-04 2014-02-03 12
Bock 18 2-D-23 2014-02-04 1
Bock 18 1-B-11 2014-02-05 4
Bock 18 1-A-02 2014-02-06 18
Order locations for each item by purchase date
Visually easy to see what we need to pick
18 Ale of the oldest and 6 of the next and so on

select o.item
, o.qty ord_qty
, i.loc
, i.purch
, i.qty loc_qty
, sum(i.qty) over (
partition by i.item
order by i.purch, i.loc
rows between unbounded preceding and 1 preceding
) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
where o.ordno = 42
order by o.item, i.purch, i.loc;
Rolling sum of previous
If the sum of all previous rows is greater than or equal to the
ordered quantity, we have picked sufficient and can stop
Analytic sum
Partition for each item
Order by date
Rolling sum of all
previous rows

ITEM ORD_QTY LOC PURCH LOC_QTY SUM_PRV_QTY
----- ------- ------- ---------- ------- -----------
Ale 24 1-A-20 2014-02-01 18
Ale 24 2-A-02 2014-02-02 24 18
Ale 24 1-C-05 2014-02-03 18 42
Ale 24 2-D-07 2014-02-04 9 60
Ale 24 1-A-31 2014-02-05 12 69
Bock 18 1-B-15 2014-02-02 2
Bock 18 1-C-04 2014-02-03 12 2
Bock 18 2-D-23 2014-02-04 1 14
Bock 18 1-B-11 2014-02-05 4 15
Bock 18 1-A-02 2014-02-06 18 19
Rolling sum of previous
Each row can now evaluate if sufficient has been picked
If the sum of all previous rows is less than the ordered quantity
we still need to pick something and the row is needed

select s.*
, least(s.loc_qty, s.ord_qty - s.sum_prv_qty) pick_qty
from (
select o.item
, o.qty ord_qty
, i.loc
, i.purch
, i.qty loc_qty
, nvl(sum(i.qty) over (
partition by i.item
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
where o.ordno = 42
) s
where s.sum_prv_qty < s.ord_qty
order by s.item, s.purch, s.loc;
Filter on previous
Keep only rows where we still need
something to pick
Pick location quantity or
what is still needed,
whichever is smallest
Set NULL in first row of
partition to 0 otherwise
predicate will fail

ITEM ORD_QTY LOC PURCH LOC_QTY SUM_PRV_QTY PICK_QTY
----- ------- ------- ---------- ------- ----------- --------
Ale 24 1-A-20 2014-02-01 18 0 18
Ale 24 2-A-02 2014-02-02 24 18 6
Bock 18 1-B-15 2014-02-02 2 0 2
Bock 18 1-C-04 2014-02-03 12 2 12
Bock 18 2-D-23 2014-02-04 1 14 1
Bock 18 1-B-11 2014-02-05 4 15 3
Filter on previous
We have now selected the necessary
inventory quantities to fulfil the order
and pick the oldest items first (FIFO)

Picklist – FIFO
select s.loc
, s.item
from (
select o.item
, o.qty ord_qty
, i.loc
, i.purch
, i.qty loc_qty
partition by i.item
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
where o.ordno = 42
) s
order by s.loc;
LOC ITEM PICK_QTY
------- ----- --------
1-A-20 Ale 18
1-B-11 Bock 3
1-B-15 Bock 2
1-C-04 Bock 12
2-A-02 Ale 6
2-D-23 Bock 1
Simple FIFO picklist
Item and quantity to pick
By location order

Picklist – Shortest route
select s.loc
, s.item
from (
select o.item
, o.qty ord_qty
, i.loc
, i.purch
, i.qty loc_qty
partition by i.item
order by i.loc -- << only line changed
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
where o.ordno = 42
) s
order by s.loc;
LOC ITEM PICK_QTY
------- ----- --------
1-A-02 Bock 18
1-A-20 Ale 18
1-A-31 Ale 6
Switch picking strategy =
Switch analytic order by
Keep "outer" order by

Picklist – Least number of picks
select s.loc
, s.item
from (
select o.item
, o.qty ord_qty
, i.loc
, i.purch
, i.qty loc_qty
partition by i.item
order by i.qty desc, i.loc -- << only line changed
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
where o.ordno = 42
) s
order by s.loc;
LOC ITEM PICK_QTY
------- ----- --------
1-A-02 Bock 18
2-A-02 Ale 24

Picklist – Clean out small quantities
select s.loc
, s.item
from (
select o.item
, o.qty ord_qty
, i.loc
, i.purch
, i.qty loc_qty
partition by i.item
order by i.qty, i.loc -- << only line changed
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
where o.ordno = 42
) s
order by s.loc;
LOC ITEM PICK_QTY
------- ----- --------
1-A-20 Ale 3
1-A-31 Ale 12
1-B-11 Bock 4
1-B-15 Bock 2
1-C-04 Bock 11
2-D-07 Ale 9
2-D-23 Bock 1

Not the greatest picking route
Strategy "Clean out small quantities" give most number of picks
We use that for picking route demonstration

select to_number(substr(s.loc,1,1)) warehouse
, substr(s.loc,3,1) aisle
, to_number(substr(s.loc,5,2)) position
, s.loc
, s.item
from (
select o.item
, o.qty ord_qty
, i.loc
, i.purch
, i.qty loc_qty
partition by i.item
order by i.qty, i.loc
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
where o.ordno = 42
) s
order by s.loc;
Warehouse, aisle and position
Split location
in parts
- Warehouse
- Aisle
- Position

WAREHOUSE AISLE POSITION LOC ITEM PICK_QTY
--------- ----- -------- ------- ----- --------
1 A 20 1-A-20 Ale 3
1 A 31 1-A-31 Ale 12
1 B 11 1-B-11 Bock 4
1 B 15 1-B-15 Bock 2
1 C 4 1-C-04 Bock 11
2 D 7 2-D-07 Ale 9
2 D 23 2-D-23 Bock 1
Warehouse, aisle and position
Warehouse, aisle and position might be from
lookup tables instead – here is simple substr
for demonstration purposes

, dense_rank() over (
order by to_number(substr(s.loc,1,1)) -- warehouse
, substr(s.loc,3,1) -- aisle
) aisle_no
, s.loc
, s.item
from (
select o.item, o.qty ord_qty, i.loc, i.purch, i.qty loc_qty
partition by i.item
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
where o.ordno = 42
) s
order by s.loc;
Consecutive numbering of aisles
Dense rank
gives equal rank
to rows with
same values in
the order by and
each rank is
one higher than
the previous

WAREHOUSE AISLE AISLE_NO POSITION LOC ITEM PICK_QTY
--------- ----- -------- -------- ------- ----- --------
1 A 1 20 1-A-20 Ale 3
1 A 1 31 1-A-31 Ale 12
1 B 2 11 1-B-11 Bock 4
1 B 2 15 1-B-15 Bock 2
1 C 3 4 1-C-04 Bock 11
2 D 4 7 2-D-07 Ale 9
2 D 4 23 2-D-23 Bock 1
Consecutive numbering of aisles
Aisles get consecutive
numbering in the order
they are visited

select s2.warehouse, s2.aisle, s2.aisle_no, s2.position
, s2.loc, s2.item, s2.pick_qty
from (
order by to_number(substr(s.loc,1,1)) -- warehouse
, substr(s.loc,3,1) -- aisle
) aisle_no
, s.loc, s.item
from (
partition by i.item
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
where o.ordno = 42
) s
) s2
order by s2.warehouse
, s2.aisle_no
, case
when mod(s2.aisle_no,2) = 1 then s2.position
else -s2.position
end;
Odd / even ordering
We order the positions
in "odd" aisles "upward" and
in "even" aisles "downward"

--------- ----- -------- -------- ------- ----- --------
1 A 1 20 1-A-20 Ale 3
1 A 1 31 1-A-31 Ale 12
1 B 2 15 1-B-15 Bock 2
1 B 2 11 1-B-11 Bock 4
1 C 3 4 1-C-04 Bock 11
2 D 4 23 2-D-23 Bock 1
2 D 4 7 2-D-07 Ale 9
Odd / even ordering
The desired ordering
– the first aisle by position ascending
– the second aisle by position descending
– and so on

select s2.warehouse, s2.aisle, s2.aisle_no, s2.position
, s2.loc, s2.item, s2.pick_qty
from (
partition by to_number(substr(s.loc,1,1)) -- warehouse
order by substr(s.loc,3,1) -- aisle
) aisle_no
, s.loc, s.item
from (
partition by i.item
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
where o.ordno = 42
) s
) s2
order by s2.warehouse
, s2.aisle_no
, case
when mod(s2.aisle_no,2) = 1 then s2.position
else -s2.position
end;
Restart count if only one door
Partition by warehouse

--------- ----- -------- -------- ------- ----- --------
1 A 1 20 1-A-20 Ale 3
1 A 1 31 1-A-31 Ale 12
1 B 2 15 1-B-15 Bock 2
1 B 2 11 1-B-11 Bock 4
1 C 3 4 1-C-04 Bock 11
2 D 1 7 2-D-07 Ale 9
2 D 1 23 2-D-23 Bock 1
Warehouse change restarts the aisle_no counter
So the first aisle in each warehouse starts by 1
and therefore is odd and positions ordered ascending

delete orderline;
Batch pick multiple orders
Get rid of the first test order
and insert three orders of
various beers

with orderbatch as (
select o.item
, sum(o.qty) qty
from orderline o
where o.ordno in (51, 62, 73)
group by o.item
)
select s.loc, s.item, least(s.loc_qty, s.ord_qty - s.sum_prv_qty) pick_qty
from (
partition by i.item
),0) sum_prv_qty
from orderbatch o
join inventory i
on i.item = o.item
) s
order by s.loc;
Aggregate orders by item
Named subquery that is the sum
of ordered quantities by item
Use subquery in FIFO query
instead of orderline table

LOC ITEM PICK_QTY
------- ----- --------
1-A-02 Bock 5
1-A-20 Ale 18
1-B-11 Bock 4
1-B-15 Bock 2
1-C-04 Bock 12
1-C-05 Ale 6
2-A-02 Ale 24
2-D-23 Bock 1
Aggregate orders by item
Gets us a nice FIFO picklist picking the
total quantities needed by the three orders
But…
We can't see how much is for each order?

with orderbatch as (
select o.item, sum(o.qty) qty
from orderline o
where o.ordno in (51, 62, 73)
group by o.item
)
select s.loc, s.item, least(s.loc_qty, s.ord_qty - s.sum_prv_qty) pick_qty
, sum_prv_qty + 1 from_qty, least(sum_qty, ord_qty) to_qty
from (
partition by i.item
),0) sum_prv_qty
partition by i.item
rows between unbounded preceding and current row
),0) sum_qty
from orderbatch o
join inventory i
on i.item = o.item
) s
order by s.item, s.purch, s.loc;
Pick quantity intervals
Both rolling sum of
previous rows only as
well as rolling sum
including current row
Calculate from and to quantity of each pick

LOC ITEM PICK_QTY FROM_QTY TO_QTY
------- ----- -------- -------- ------
1-A-20 Ale 18 1 18
2-A-02 Ale 24 19 42
1-C-05 Ale 6 43 48
1-B-15 Bock 2 1 2
1-C-04 Bock 12 3 14
2-D-23 Bock 1 15 15
1-B-11 Bock 4 16 19
1-A-02 Bock 5 20 24
Pick quantity intervals
The 24 Ale picked at 2-A-02 is number
19-42 of the total 48 Ale we are picking

select o.ordno, o.item, o.qty
, nvl(sum(o.qty) over (
partition by o.item
order by o.ordno
),0) + 1 from_qty
partition by o.item
order by o.ordno
),0) to_qty
from orderline o
where ordno in (51, 62, 73)
order by o.item, o.ordno;
Order quantity intervals
Similarly calculate from and to
quantity of the orderlines

ORDNO ITEM QTY FROM_QTY TO_QTY
----- ----- ---- -------- ------
51 Ale 24 1 24
62 Ale 8 25 32
73 Ale 16 33 48
51 Bock 18 1 18
73 Bock 6 19 24
Order quantity intervals
The 8 Ale from order no 62 is number
25-32 of the total 48 Ale ordered

with orderlines as (
select o.ordno, o.item, o.qty
partition by o.item
order by o.ordno
),0) + 1 from_qty
partition by o.item
order by o.ordno
),0) to_qty
from orderline o
where ordno in (51, 62, 73)
), orderbatch as (
select o.item, sum(o.qty) qty
from orderlines o
group by o.item
...
Join on overlapping intervals
Named subquery with the
orderlines and their intervals
Named subquery with the
aggregate sums by item
>>>

...
), fifo as (
select s.loc, s.item, s.purch, least(s.loc_qty, s.ord_qty - s.sum_prv_qty) pick_qty
, sum_prv_qty + 1 from_qty, least(sum_qty, ord_qty) to_qty
from (
partition by i.item
),0) sum_prv_qty
partition by i.item
),0) sum_qty
from orderbatch o
join inventory i
on i.item = o.item
) s
...
Named subquery
with FIFO pick of
the sums with
quantity intervals
>>>

...
)
select f.loc, f.item, f.purch, f.pick_qty, f.from_qty, f.to_qty
, o.ordno, o.qty, o.from_qty, o.to_qty
from fifo f
join orderlines o
on o.item = f.item
and o.to_qty >= f.from_qty
and o.from_qty <= f.to_qty
order by f.item, f.purch, o.ordno;
Join the fifo subquery with the
orderlines subquery on item and
overlapping quantity intervals

LOC ITEM PURCH PICK_QTY FROM_QTY TO_QTY ORDNO QTY FROM_QTY TO_QTY
------- ----- ---------- -------- -------- ------ ----- ---- -------- ------
1-A-20 Ale 2014-02-01 18 1 18 51 24 1 24
2-A-02 Ale 2014-02-02 24 19 42 51 24 1 24
2-A-02 Ale 2014-02-02 24 19 42 62 8 25 32
2-A-02 Ale 2014-02-02 24 19 42 73 16 33 48
1-C-05 Ale 2014-02-03 6 43 48 73 16 33 48
1-B-15 Bock 2014-02-02 2 1 2 51 18 1 18
1-C-04 Bock 2014-02-03 12 3 14 51 18 1 18
2-D-23 Bock 2014-02-04 1 15 15 51 18 1 18
1-B-11 Bock 2014-02-05 4 16 19 51 18 1 18
1-B-11 Bock 2014-02-05 4 16 19 73 6 19 24
1-A-02 Bock 2014-02-06 5 20 24 73 6 19 24
At location 2-A-02 we pick number 19-42 out of 48 Ale
That overlaps with all three orders, as they get respectively
number 1-24, 25-32 and 33-48 of the 48 Ale

...
), orderbatch as (
...
), fifo as (
...
)
select f.loc, f.item, f.purch, f.pick_qty, f.from_qty, f.to_qty
, o.ordno, o.qty, o.from_qty, o.to_qty
, least(
f.loc_qty
, least(o.to_qty, f.to_qty) - greatest(o.from_qty, f.from_qty) + 1
) pick_ord_qty
from fifo f
join orderlines o
on o.item = f.item
order by f.item, f.purch, o.ordno;
How much to pick
Each row gets either the "size of the
overlap" or the quantity on the location,
whichever is smallest

LOC ITEM PURCH PICK_QTY FROM_QTY TO_QTY ORDNO QTY FROM_QTY TO_QTY PICK_ORD_QTY
------- ----- ---------- -------- -------- ------ ----- ---- -------- ------ ------------
1-A-20 Ale 2014-02-01 18 1 18 51 24 1 24 18
2-A-02 Ale 2014-02-02 24 19 42 51 24 1 24 6
2-A-02 Ale 2014-02-02 24 19 42 62 8 25 32 8
2-A-02 Ale 2014-02-02 24 19 42 73 16 33 48 10
1-C-05 Ale 2014-02-03 6 43 48 73 16 33 48 6
1-B-15 Bock 2014-02-02 2 1 2 51 18 1 18 2
1-C-04 Bock 2014-02-03 12 3 14 51 18 1 18 12
2-D-23 Bock 2014-02-04 1 15 15 51 18 1 18 1
1-B-11 Bock 2014-02-05 4 16 19 51 18 1 18 3
1-B-11 Bock 2014-02-05 4 16 19 73 6 19 24 1
1-A-02 Bock 2014-02-06 5 20 24 73 6 19 24 5
How much to pick
At 2-A-02 we pick 6 Ale to order 51, 8 Ale to order 62
and 10 Ale to order 73 – total 24 Ale from that location

Batch picklist – FIFO
...
), orderbatch as (
...
), fifo as (
...
)
select f.loc, f.item
, f.pick_qty pick_at_loc, o.ordno
, least(
f.loc_qty
, least(o.to_qty, f.to_qty)
- greatest(o.from_qty, f.from_qty) + 1
) qty_for_ord
from fifo f
join orderlines o
on o.item = f.item
order by f.loc, o.ordno;
LOC ITEM PICK_AT_LOC ORDNO QTY_FOR_ORD
------- ----- ----------- ----- -----------
1-A-02 Bock 5 73 5
1-A-20 Ale 18 51 18
1-B-11 Bock 4 51 3
1-B-11 Bock 4 73 1
1-B-15 Bock 2 51 2
1-C-04 Bock 12 51 12
1-C-05 Ale 6 73 6
2-A-02 Ale 24 51 6
2-A-02 Ale 24 62 8
2-A-02 Ale 24 73 10
2-D-23 Bock 1 51 1
Clean up query and keep what's
needed for picking operator

...
), orderbatch as (
...
), fifo as (
...
), pick as (
select to_number(substr(f.loc,1,1)) warehouse
, substr(f.loc,3,1) aisle
order by to_number(substr(f.loc,1,1)) -- warehouse
, substr(f.loc,3,1) -- aisle
) aisle_no
, to_number(substr(f.loc,5,2)) position
, f.loc, f.item, f.pick_qty pick_at_loc, o.ordno
, least(
f.loc_qty
, least(o.to_qty, f.to_qty) - greatest(o.from_qty, f.from_qty) + 1
) qty_for_ord
from fifo f
join orderlines o
on o.item = f.item
...
Batch picklist FIFO with picking route
Named subquery with batch picklist adding
warehouse, aisle, position and dense rank
>>>

Batch picklist FIFO with picking route
...
)
select p.loc, p.item, p.pick_at_loc
, p.ordno, p.qty_for_ord
from pick p
order by p.warehouse
, p.aisle_no
, case
when mod(p.aisle_no,2) = 1 then
p.position
else
-p.position
end;
LOC ITEM PICK_AT_LOC ORDNO QTY_FOR_ORD
------- ----- ----------- ----- -----------
1-A-02 Bock 5 73 5
1-A-20 Ale 18 51 18
1-B-15 Bock 2 51 2
1-B-11 Bock 4 51 3
1-B-11 Bock 4 73 1
1-C-04 Bock 12 51 12
1-C-05 Ale 6 73 6
2-A-02 Ale 24 51 6
2-A-02 Ale 24 73 10
2-A-02 Ale 24 62 8
2-D-23 Bock 1 51 1
Select from the pick subquery
Ordering with odd / even logic
Batch picklist by FIFO with
picking route in single SQL
finished 

 FIFO principle
● Or other principles by changing one line of code
 Picking route
● Up and down alternate aisles
 Batch picking
● Multiple orders simultaneously
 Single SQL picking
 Done 
Case closed

 Picking by FIFO
Sales forecasting
Cases

 Sales forecasting
● Seasonal items (summer / winter)
● Trending upwards or downwards over time
 Regression
● Simple model ”transposing graph”
● Datascientists model ”Time Series Analysis”
 Single SQL
● Utilize the power of the database
Case: Sales forecasting

create table sales (
item varchar2(10)
, mth date
, qty number
);
insert into sales values ('Snowchain', date '2011-01-01', 79);
...
insert into sales values ('Sunshade' , date '2011-01-01', 4);
...
Sales 2011 – 2013
Monthly sales
2011 - 2013
Snowchain and
Sunshade

Snowchain peaks wintertime and trends upwards
Sunshade peaks summertime and trends downwards

select sales.item, sales.mth, sales.qty
, regr_slope(
sales.qty
, extract(year from sales.mth) * 12 + extract(month from sales.mth)
) over (
partition by sales.item
order by sales.mth
range between interval '23' month preceding and current row
) slope
from sales
order by sales.item, sales.mth;
Moving slope
Calculate slope of linear regression of a graph with qty on the Y
axis and month as number with unit 1=month on the X axis
"Rolling" slope of 24 points on the graph (= 2 years)

Moving slope
ITEM MTH QTY SLOPE
---------- ------- ---- -------
Snowchain 2011-01 79
Snowchain 2011-02 133 54.000
Snowchain 2011-03 24 -27.500
Snowchain 2011-04 1 -34.300
Snowchain 2011-05 0 -29.000
Snowchain 2011-06 0 -23.343
...
Snowchain 2013-01 167 1.776
Snowchain 2013-02 247 4.821
Snowchain 2013-03 42 4.533
Snowchain 2013-04 0 3.558
Snowchain 2013-05 0 2.574
Snowchain 2013-06 0 1.590
Snowchain 2013-07 0 .605
Snowchain 2013-08 1 -.369
Snowchain 2013-09 0 -1.343
Snowchain 2013-10 1 -2.274
Snowchain 2013-11 73 -2.363
Snowchain 2013-12 160 -.991
ITEM MTH QTY SLOPE
---------- ------- ---- -------
Sunshade 2011-01 4
Sunshade 2011-02 6 2.000
Sunshade 2011-03 32 14.000
Sunshade 2011-04 45 14.900
Sunshade 2011-05 62 15.500
Sunshade 2011-06 58 12.886
...
Sunshade 2013-01 2 -1.135
Sunshade 2013-02 8 -1.595
Sunshade 2013-03 28 -1.574
Sunshade 2013-04 26 -1.428
Sunshade 2013-05 23 -1.111
Sunshade 2013-06 46 -.574
Sunshade 2013-07 73 .537
Sunshade 2013-08 25 .560
Sunshade 2013-09 13 .421
Sunshade 2013-10 11 .217
Sunshade 2013-11 3 -.200
Sunshade 2013-12 5 -.574
Slopes in
2011 not
very useful
Slopes in
2013 based
on 2 years
data

Slope of each month is not the same

select item, mth, qty
, qty + 12 * slope qty_next_year
from (
, regr_slope(
sales.qty
) over (
order by sales.mth
) slope
from sales
)
where mth >= date '2013-01-01'
order by item, mth;
Transpose 12 months
Filter 2013 with
the useful slopes
Slope is Y-increment per month
12 * Slope is Y-increment per year
Add 12 * Slope to Qty is forecast

Transpose 12 months
ITEM MTH QTY QTY_NEXT_YEAR
---------- ------- ---- -------------
Snowchain 2013-01 167 188.3130
Snowchain 2013-02 247 304.8557
Snowchain 2013-03 42 96.3913
Snowchain 2013-04 0 42.6991
Snowchain 2013-05 0 30.8870
Snowchain 2013-06 0 19.0748
Snowchain 2013-07 0 7.2626
Snowchain 2013-08 1 -3.4296
Snowchain 2013-09 0 -16.1217
Snowchain 2013-10 1 -26.2922
Snowchain 2013-11 73 44.6435
Snowchain 2013-12 160 148.1096
ITEM MTH QTY QTY_NEXT_YEAR
---------- ------- ---- -------------
Sunshade 2013-01 2 -11.6174
Sunshade 2013-02 8 -11.1374
Sunshade 2013-03 28 9.1130
Sunshade 2013-04 26 8.8609
Sunshade 2013-05 23 9.6643
Sunshade 2013-06 46 39.1130
Sunshade 2013-07 73 79.4487
Sunshade 2013-08 25 31.7148
Sunshade 2013-09 13 18.0504
Sunshade 2013-10 11 13.6087
Sunshade 2013-11 3 .5948
Sunshade 2013-12 5 -1.8870
Each month is transposed 12 months into the future
by the slope of the previous 24 months

select item
, add_months(mth, 12) mth
, greatest(round(qty + 12 * slope), 0) forecast
from (
, regr_slope(
sales.qty
) over (
order by sales.mth
) slope
from sales
)
order by item, mth;
Rounded forecast
Rather than "qty_next_year", add 12 months
to show the month of the forecast
Round off to whole quantities and assume
negative forecast is a zero sale

Rounded forecast
ITEM MTH FORECAST
---------- ------- --------
Snowchain 2014-07 7
Snowchain 2014-08 0
Snowchain 2014-09 0
Snowchain 2014-10 0
ITEM MTH FORECAST
---------- ------- --------
Sunshade 2014-01 0
Sunshade 2014-02 0
Sunshade 2014-03 9
Sunshade 2014-04 9
Sunshade 2014-05 10
Sunshade 2014-06 39
Sunshade 2014-07 79
Sunshade 2014-08 32
Sunshade 2014-09 18
Sunshade 2014-10 14
Sunshade 2014-11 1
Sunshade 2014-12 0
Simple forecast with nice numbers

select item, mth, qty, type
, sum(qty) over (partition by item, extract(year from mth)) qty_yr
from (
select sales.item, sales.mth, sales.qty, 'Actual' type
from sales
union all
select item, add_months(mth, 12) mth
, greatest(round(qty + 12 * slope), 0) qty, 'Forecast' type
from (
, regr_slope(
sales.qty
) over (
order by sales.mth
) slope
from sales
)
)
order by item, mth;
Sales and forecast
Union actual sales
with forecast
Show year totals
for comparison

Sales and forecast
ITEM MTH QTY TYPE QTY_YR
---------- ------- ---- -------- ------
Snowchain 2011-01 79 Actual 331
...
...
...
Snowchain 2014-01 188 Forecast 882
---------- ------- ---- -------- ------
Sunshade 2011-01 4 Actual 377
...
...
...
Sunshade 2014-01 0 Forecast 211
Year totals
easy to
see trend
Snowchain
upward,
Sunshade
downward

Forecast follow
trend up or down
but keep graph
shape (seasons)

 Our dataanalyst/scientist did a model in Excel
● Centered Moving Average
● Seasonality
● Deseasonalize
● Regression trend
● Reseasonalize
 http://people.duke.edu/~rnau/411outbd.htm
 OK, I can do that in a SQL statement…
Time Series Analysis

select sales.item
, mths.ts
, mths.mth
, extract(year from mths.mth) yr
, extract(month from mths.mth) mthno
, sales.qty
from (
select add_months(date '2011-01-01', level-1) mth
, level ts --time serie
from dual
connect by level <= 48
) mths
left outer join sales
partition by (sales.item)
on sales.mth = mths.mth
order by sales.item, mths.mth;
Time Series
Create 48 month time
series for each item –
sales 2011-13 and
forecast 2014
Partitioned outer join
gives rows for 2014 for
each item with null qty

Time Series
ITEM TS MTH YR MTHNO QTY
---------- --- ------- ----- ----- ----
Snowchain 1 2011-01 2011 1 79
Snowchain 2 2011-02 2011 2 133
Snowchain 3 2011-03 2011 3 24
...
Snowchain 34 2013-10 2013 10 1
Snowchain 35 2013-11 2013 11 73
Snowchain 36 2013-12 2013 12 160
Snowchain 37 2014-01 2014 1
Snowchain 38 2014-02 2014 2
Snowchain 39 2014-03 2014 3
Snowchain 40 2014-04 2014 4
Snowchain 41 2014-05 2014 5
Snowchain 42 2014-06 2014 6
Snowchain 43 2014-07 2014 7
Snowchain 44 2014-08 2014 8
Snowchain 45 2014-09 2014 9
Snowchain 46 2014-10 2014 10
Snowchain 47 2014-11 2014 11
Snowchain 48 2014-12 2014 12
ITEM TS MTH YR MTHNO QTY
---------- --- ------- ----- ----- ----
Sunshade 1 2011-01 2011 1 4
Sunshade 2 2011-02 2011 2 6
Sunshade 3 2011-03 2011 3 32
...
Sunshade 34 2013-10 2013 10 11
Sunshade 35 2013-11 2013 11 3
Sunshade 36 2013-12 2013 12 5
Sunshade 37 2014-01 2014 1
Sunshade 38 2014-02 2014 2
Sunshade 39 2014-03 2014 3
Sunshade 40 2014-04 2014 4
Sunshade 41 2014-05 2014 5
Sunshade 42 2014-06 2014 6
Sunshade 43 2014-07 2014 7
Sunshade 44 2014-08 2014 8
Sunshade 45 2014-09 2014 9
Sunshade 46 2014-10 2014 10
Sunshade 47 2014-11 2014 11
Sunshade 48 2014-12 2014 12
Sales in 2011-13, null qty in 2014

with s1 as (
...
)
select s1.*
, case when ts between 7 and 30
then
(nvl(avg(qty) over (
partition by item
order by ts
rows between 5 preceding and 6 following
),0) + nvl(avg(qty) over (
partition by item
order by ts
rows between 6 preceding and 5 following
),0)) / 2
else
null
end cma -- centered moving average
from s1
order by item, ts;
Centered Moving Average
• Rolling average
-5 to +6 months
• Rolling average
-6 to +5 months
Average of those
two is CMA
Do this only for
those months
(ts 7-30) where
there is 12 months
data

Centered Moving Average
ITEM TS MTH YR MTHNO QTY CMA
---------- --- ------- ----- ----- ---- -------
Snowchain 1 2011-01 2011 1 79
Snowchain 2 2011-02 2011 2 133
Snowchain 3 2011-03 2011 3 24
Snowchain 4 2011-04 2011 4 1
Snowchain 5 2011-05 2011 5 0
Snowchain 6 2011-06 2011 6 0
Snowchain 7 2011-07 2011 7 0 30.458
Snowchain 8 2011-08 2011 8 0 36.500
Snowchain 9 2011-09 2011 9 1 39.917
Snowchain 10 2011-10 2011 10 4 40.208
Snowchain 11 2011-11 2011 11 15 40.250
Snowchain 12 2011-12 2011 12 74 40.250
Snowchain 13 2012-01 2012 1 148 40.250
Snowchain 14 2012-02 2012 2 209 40.292
Snowchain 15 2012-03 2012 3 30 40.292
...
Snowchain 29 2013-05 2013 5 0 56.250
Snowchain 30 2013-06 2013 6 0 58.083
Snowchain 31 2013-07 2013 7 0
Snowchain 32 2013-08 2013 8 1
Snowchain 33 2013-09 2013 9 0
...
ITEM TS MTH YR MTHNO QTY CMA
---------- --- ------- ----- ----- ---- -------
Sunshade 1 2011-01 2011 1 4
Sunshade 2 2011-02 2011 2 6
Sunshade 3 2011-03 2011 3 32
Sunshade 4 2011-04 2011 4 45
Sunshade 5 2011-05 2011 5 62
Sunshade 6 2011-06 2011 6 58
Sunshade 7 2011-07 2011 7 85 31.333
Sunshade 8 2011-08 2011 8 28 31.542
Sunshade 9 2011-09 2011 9 24 31.708
Sunshade 10 2011-10 2011 10 19 32.208
Sunshade 11 2011-11 2011 11 6 31.458
Sunshade 12 2011-12 2011 12 8 30.917
Sunshade 13 2012-01 2012 1 2 30.542
Sunshade 14 2012-02 2012 2 13 29.083
Sunshade 15 2012-03 2012 3 29 28.292
...
Sunshade 29 2013-05 2013 5 23 21.833
Sunshade 30 2013-06 2013 6 46 21.833
Sunshade 31 2013-07 2013 7 73
Sunshade 32 2013-08 2013 8 25
Sunshade 33 2013-09 2013 9 13
...
CMA for 24 months
2011-07 to 2013-06

with s1 as (
...
), s2 as (
...
)
select s2.*
, nvl(avg(
case qty when 0 then 0.0001 else qty end / nullif(cma,0)
) over (
partition by item, mthno
),0) s -- seasonality
from s2
order by item, ts;
Seasonality factor
Qty divided by CMA factor
Average factor of the
month is seasonality
Partition makes seasonality
same for all january, all
february, etc. (by item)

Seasonality factor
ITEM TS MTH YR MTHNO QTY CMA S
---------- --- ------- ----- ----- ---- ------- -------
...
Snowchain 9 2011-09 2011 9 1 39.917 .0125
Snowchain 10 2011-10 2011 10 4 40.208 .0774
Snowchain 11 2011-11 2011 11 15 40.250 .3435
Snowchain 12 2011-12 2011 12 74 40.250 2.5094
Snowchain 13 2012-01 2012 1 148 40.250 3.3824
Snowchain 14 2012-02 2012 2 209 40.292 4.8771
Snowchain 15 2012-03 2012 3 30 40.292 .7606
Snowchain 16 2012-04 2012 4 2 40.208 .0249
Snowchain 17 2012-05 2012 5 0 40.250 .0000
Snowchain 18 2012-06 2012 6 0 44.417 .0000
Snowchain 19 2012-07 2012 7 0 49.292 .0000
Snowchain 20 2012-08 2012 8 1 51.667 .0097
Snowchain 21 2012-09 2012 9 0 53.750 .0125
Snowchain 22 2012-10 2012 10 3 54.167 .0774
Snowchain 23 2012-11 2012 11 17 54.083 .3435
Snowchain 24 2012-12 2012 12 172 54.083 2.5094
Snowchain 25 2013-01 2013 1 167 54.083 3.3824
Snowchain 26 2013-02 2013 2 247 54.083 4.8771
Snowchain 27 2013-03 2013 3 42 54.083 .7606
Snowchain 28 2013-04 2013 4 0 54.000 .0249
...
ITEM TS MTH YR MTHNO QTY CMA S
---------- --- ------- ----- ----- ---- ------- -------
...
Sunshade 9 2011-09 2011 9 24 31.708 .5876
Sunshade 10 2011-10 2011 10 19 32.208 .5567
Sunshade 11 2011-11 2011 11 6 31.458 .2033
Sunshade 12 2011-12 2011 12 8 30.917 .1989
Sunshade 13 2012-01 2012 1 2 30.542 .0805
Sunshade 14 2012-02 2012 2 13 29.083 .4071
Sunshade 15 2012-03 2012 3 29 28.292 1.1489
Sunshade 16 2012-04 2012 4 60 27.500 1.6818
Sunshade 17 2012-05 2012 5 29 27.208 1.0596
Sunshade 18 2012-06 2012 6 78 26.958 2.5001
Sunshade 19 2012-07 2012 7 56 26.750 2.4031
Sunshade 20 2012-08 2012 8 22 26.542 .8583
Sunshade 21 2012-09 2012 9 11 26.292 .5876
Sunshade 22 2012-10 2012 10 13 24.833 .5567
Sunshade 23 2012-11 2012 11 5 23.167 .2033
Sunshade 24 2012-12 2012 12 3 21.583 .1989
Sunshade 25 2013-01 2013 1 2 20.958 .0805
Sunshade 26 2013-02 2013 2 8 21.792 .4071
Sunshade 27 2013-03 2013 3 28 22.000 1.1489
Sunshade 28 2013-04 2013 4 26 22.000 1.6818
...
S calculated for each month
Partition makes it repeat

with s1 as (
...
), s2 as (
...
), s3 as (
...
)
select s3.*
, case when ts <= 36 then
nvl(case qty when 0 then 0.0001 else qty end / nullif(s,0), 0)
end des -- deseasonalized
from s3
order by item, ts;
Deseasonalized quantity
Divide each individual qty by the seasonality factor

Deseasonalized quantity
ITEM TS MTH QTY CMA S DES
---------- --- ------- ---- ------- ------- --------
Snowchain 1 2011-01 79 3.3824 23.356
Snowchain 2 2011-02 133 4.8771 27.270
Snowchain 3 2011-03 24 .7606 31.555
...
Snowchain 22 2012-10 3 54.167 .0774 38.743
Snowchain 23 2012-11 17 54.083 .3435 49.490
Snowchain 24 2012-12 172 54.083 2.5094 68.542
Snowchain 25 2013-01 167 54.083 3.3824 49.373
Snowchain 26 2013-02 247 54.083 4.8771 50.645
Snowchain 27 2013-03 42 54.083 .7606 55.221
Snowchain 28 2013-04 0 54.000 .0249 .004
Snowchain 29 2013-05 0 56.250 .0000 46.924
Snowchain 30 2013-06 0 58.083 .0000 50.339
Snowchain 31 2013-07 0 .0000 37.651
Snowchain 32 2013-08 1 .0097 103.319
Snowchain 33 2013-09 0 .0125 .008
Snowchain 34 2013-10 1 .0774 12.914
Snowchain 35 2013-11 73 .3435 212.518
Snowchain 36 2013-12 160 2.5094 63.760
...
ITEM TS MTH QTY CMA S DES
---------- --- ------- ---- ------- ------- --------
Sunshade 1 2011-01 4 .0805 49.717
Sunshade 2 2011-02 6 .4071 14.740
Sunshade 3 2011-03 32 1.1489 27.853
...
Sunshade 22 2012-10 13 24.833 .5567 23.352
Sunshade 23 2012-11 5 23.167 .2033 24.597
Sunshade 24 2012-12 3 21.583 .1989 15.085
Sunshade 25 2013-01 2 20.958 .0805 24.858
Sunshade 26 2013-02 8 21.792 .4071 19.654
Sunshade 27 2013-03 28 22.000 1.1489 24.372
Sunshade 28 2013-04 26 22.000 1.6818 15.459
Sunshade 29 2013-05 23 21.833 1.0596 21.705
Sunshade 30 2013-06 46 21.833 2.5001 18.399
Sunshade 31 2013-07 73 2.4031 30.377
Sunshade 32 2013-08 25 .8583 29.127
Sunshade 33 2013-09 13 .5876 22.122
Sunshade 34 2013-10 11 .5567 19.759
Sunshade 35 2013-11 3 .2033 14.758
Sunshade 36 2013-12 5 .1989 25.141
...
DES show trend more clear
without seasonal up/down

with s1 as (
...
), s2 as (
...
), s3 as (
...
), s4 as (
...
)
select s4.*
, regr_intercept(des,ts) over (partition by item)
+ ts*regr_slope(des,ts) over (partition by item) t -- trend
from s4
order by item, ts;
Trend (regression)
Linear regression of deseasonalized qty
gives the trend line
Intercept is the point where the line intersects Y axis
Add slope*time series (month) and get Y value of trend line

Trend (regression)
ITEM TS MTH QTY CMA S DES T
---------- --- ------- ---- ------- ------- -------- -------
Snowchain 1 2011-01 79 3.3824 23.356 32.163
Snowchain 2 2011-02 133 4.8771 27.270 33.096
Snowchain 3 2011-03 24 .7606 31.555 34.030
...
Snowchain 34 2013-10 1 .0774 12.914 62.976
Snowchain 35 2013-11 73 .3435 212.518 63.910
Snowchain 36 2013-12 160 2.5094 63.760 64.844
Snowchain 37 2014-01 3.3824 65.777
Snowchain 38 2014-02 4.8771 66.711
Snowchain 39 2014-03 .7606 67.645
Snowchain 40 2014-04 .0249 68.579
Snowchain 41 2014-05 .0000 69.512
Snowchain 42 2014-06 .0000 70.446
Snowchain 43 2014-07 .0000 71.380
Snowchain 44 2014-08 .0097 72.314
Snowchain 45 2014-09 .0125 73.247
Snowchain 46 2014-10 .0774 74.181
Snowchain 47 2014-11 .3435 75.115
Snowchain 48 2014-12 2.5094 76.049
ITEM TS MTH QTY CMA S DES T
---------- --- ------- ---- ------- ------- -------- -------
Sunshade 1 2011-01 4 .0805 49.717 35.860
Sunshade 2 2011-02 6 .4071 14.740 35.376
Sunshade 3 2011-03 32 1.1489 27.853 34.892
...
Sunshade 34 2013-10 11 .5567 19.759 19.895
Sunshade 35 2013-11 3 .2033 14.758 19.412
Sunshade 36 2013-12 5 .1989 25.141 18.928
Sunshade 37 2014-01 .0805 18.444
Sunshade 38 2014-02 .4071 17.960
Sunshade 39 2014-03 1.1489 17.477
Sunshade 40 2014-04 1.6818 16.993
Sunshade 41 2014-05 1.0596 16.509
Sunshade 42 2014-06 2.5001 16.025
Sunshade 43 2014-07 2.4031 15.542
Sunshade 44 2014-08 .8583 15.058
Sunshade 45 2014-09 .5876 14.574
Sunshade 46 2014-10 .5567 14.090
Sunshade 47 2014-11 .2033 13.606
Sunshade 48 2014-12 .1989 13.123
Linear trend line from
2011 to 2014

with s1 as (
...
), s2 as (
...
), s3 as (
...
), s4 as (
...
), s5 as (
...
)
select s5.*
, t * s forecast --reseasonalized
from s5
order by item, ts;
Reseasonalize (forecast)
Multiply trend line by seasonality factor
and get the qty forecasted by the model

Reseasonalize (forecast)
ITEM TS MTH QTY S DES T FORECAST
---------- --- ------- ---- ------- -------- ------- --------
Snowchain 1 2011-01 79 3.3824 23.356 32.163 108.788
Snowchain 2 2011-02 133 4.8771 27.270 33.096 161.414
Snowchain 3 2011-03 24 .7606 31.555 34.030 25.882
Snowchain 4 2011-04 1 .0249 40.207 34.964 .870
...
Snowchain 33 2013-09 0 .0125 .008 62.042 .777
Snowchain 34 2013-10 1 .0774 12.914 62.976 4.876
Snowchain 35 2013-11 73 .3435 212.518 63.910 21.953
Snowchain 36 2013-12 160 2.5094 63.760 64.844 162.718
Snowchain 37 2014-01 3.3824 65.777 222.487
Snowchain 38 2014-02 4.8771 66.711 325.357
Snowchain 39 2014-03 .7606 67.645 51.449
Snowchain 40 2014-04 .0249 68.579 1.706
Snowchain 41 2014-05 .0000 69.512 .000
Snowchain 42 2014-06 .0000 70.446 .000
Snowchain 43 2014-07 .0000 71.380 .000
Snowchain 44 2014-08 .0097 72.314 .700
Snowchain 45 2014-09 .0125 73.247 .918
Snowchain 46 2014-10 .0774 74.181 5.744
Snowchain 47 2014-11 .3435 75.115 25.802
Snowchain 48 2014-12 2.5094 76.049 190.836
ITEM TS MTH QTY S DES T FORECAST
---------- --- ------- ---- ------- -------- ------- --------
Sunshade 1 2011-01 4 .0805 49.717 35.860 2.885
Sunshade 2 2011-02 6 .4071 14.740 35.376 14.400
Sunshade 3 2011-03 32 1.1489 27.853 34.892 40.087
Sunshade 4 2011-04 45 1.6818 26.757 34.409 57.869
...
Sunshade 33 2013-09 13 .5876 22.122 20.379 11.976
Sunshade 34 2013-10 11 .5567 19.759 19.895 11.076
Sunshade 35 2013-11 3 .2033 14.758 19.412 3.946
Sunshade 36 2013-12 5 .1989 25.141 18.928 3.764
Sunshade 37 2014-01 .0805 18.444 1.484
Sunshade 38 2014-02 .4071 17.960 7.311
Sunshade 39 2014-03 1.1489 17.477 20.079
Sunshade 40 2014-04 1.6818 16.993 28.579
Sunshade 41 2014-05 1.0596 16.509 17.494
Sunshade 42 2014-06 2.5001 16.025 40.065
Sunshade 43 2014-07 2.4031 15.542 37.348
Sunshade 44 2014-08 .8583 15.058 12.924
Sunshade 45 2014-09 .5876 14.574 8.564
Sunshade 46 2014-10 .5567 14.090 7.844
Sunshade 47 2014-11 .2033 13.606 2.766
Sunshade 48 2014-12 .1989 13.123 2.610
Trend line linear
Apply seasonality
= forecast

Snowchain reseasonalized forecast
Compare to actual data 2011-13

Sunshade reseasonalized forecast
Compare to actual data 2011-13

with s1 as (
...
), s2 as (
...
), s3 as (
...
), s4 as (
...
), s5 as (
...
)
select item
, mth
, qty
, t * s forecast --reseasonalized
, sum(qty) over (partition by item, yr) qty_yr
, sum(t * s) over (partition by item, yr) fc_yr
from s5
order by item, ts;
Model describes reality?
2011-13 qty and forecast can be
compared to see how well model
described reality

ITEM MTH QTY FORECAST QTY_YR FC_YR
---------- ------- ---- -------- ------ -------
Snowchain 2011-01 79 108.788 331 421.70
...
Snowchain 2012-01 148 146.687 582 556.14
...
Snowchain 2013-01 167 184.587 691 690.57
Snowchain 2013-02 247 270.710 691 690.57
Snowchain 2013-03 42 42.927 691 690.57
Snowchain 2013-04 0 1.427 691 690.57
...
Snowchain 2013-09 0 .777 691 690.57
Snowchain 2013-10 1 4.876 691 690.57
Snowchain 2013-11 73 21.953 691 690.57
Snowchain 2013-12 160 162.718 691 690.57
Snowchain 2014-01 222.487 825.00
Snowchain 2014-02 325.357 825.00
Snowchain 2014-03 51.449 825.00
Snowchain 2014-04 1.706 825.00
Snowchain 2014-05 .000 825.00
Snowchain 2014-06 .000 825.00
Snowchain 2014-07 .000 825.00
Snowchain 2014-08 .700 825.00
Snowchain 2014-09 .918 825.00
Snowchain 2014-10 5.744 825.00
Snowchain 2014-11 25.802 825.00
Snowchain 2014-12 190.836 825.00
ITEM MTH QTY FORECAST QTY_YR FC_YR
---------- ------- ---- -------- ------ -------
Sunshade 2011-01 4 2.885 377 390.59
...
Sunshade 2012-01 2 2.418 321 322.75
...
Sunshade 2013-01 2 1.951 263 254.91
...
Sunshade 2013-04 26 38.342 263 254.91
Sunshade 2013-05 23 23.645 263 254.91
Sunshade 2013-06 46 54.579 263 254.91
Sunshade 2013-07 73 51.299 263 254.91
Sunshade 2013-08 25 17.907 263 254.91
...
Sunshade 2013-11 3 3.946 263 254.91
Sunshade 2013-12 5 3.764 263 254.91
Sunshade 2014-01 1.484 187.07
Sunshade 2014-02 7.311 187.07
Sunshade 2014-03 20.079 187.07
Sunshade 2014-04 28.579 187.07
Sunshade 2014-05 17.494 187.07
Sunshade 2014-06 40.065 187.07
Sunshade 2014-07 37.348 187.07
Sunshade 2014-08 12.924 187.07
Sunshade 2014-09 8.564 187.07
Sunshade 2014-10 7.844 187.07
Sunshade 2014-11 2.766 187.07
Sunshade 2014-12 2.610 187.07
Compare numbers
and see if model
matches reality

with s1 as (...), s2 as (...), s3 as (...), s4 as (...), s5 as (...)
select item, mth
, case
when ts <= 36 then qty
else round(t * s)
end qty
, case
when ts <= 36 then 'Actual'
else 'Forecast'
end type
, sum(
case
when ts <= 36 then qty
else round(t * s)
end
) over (
partition by item, extract(year from mth)
) qty_yr
from s5
order by item, ts;
Sales and forecast
Output of Actual and Forecast
like we did with simple model

Sales and forecast
---------- ------- ---- -------- ------
...
...
...
---------- ------- ---- -------- ------
...
...
...
This model also
follows up/down
trends

Time series slightly more conservative…

…probably
due to
negative
forecasts
in simple
model
…than simple transpose model…

 Data analyst develops predictive Time Series
Analysis model of reality in Excel
 Recreate that model in SQL for repeated
application to larger datasets
 Query where model fits reality or not
 Single SQL forecasting
 Done 
Case closed

 Danish geek
 Oracle SQL Evangelist
 Oracle PL/SQL Developer
 Likes to cook
 Reads sci-fi
 Member of
Danish Beer Enthusiasts
Questions ?
http://dspsd.blogspot.com
http://dk.linkedin.com/in/kibeha/
@kibeha
Kim Berg Hansen
http://goo.gl/q1YJRL
for this presentation
and scripts

Oracle database - Analytic functions - Advanced cases

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