Hi, all,
Again I encountered a very strange problem which displayed the predicted attribute values as percentage format? The data type of the attribute is actually double, why is that?
That's really frustrated.
Thanks a lot in advance for your kind advices and I am looking forward to hearing from you shortly.
With best regards,
Yours sincerely,
Hi, all experts here,
Thank you very much for your kind attention.
I encountered a very strange problem again. Why the time series displayed on the chart are so strange? The Key time column I chose for my time series algorithm is cal_month(e.g 199001...), but why the date displayed on the time series chart is like :05/06/2448? (it should be like 199001..?) What is that data? And where exactly did it come from? What is the exact cause of this?
Hope it is clear for your help.
I am really confused on this and thanks a lot for your kind advices and help and I am looking forward to hearing from you shortly.
With best regards,
Yours sincerely,
Hi all,
I have MS Time Seeries model using a database of over a thousand products each of which has hundreds of cases. It amazingly takes only a few minutes to finish processing the model, but when I click Mining Model Viewer to view the models, it takes many hours to show up. Once the window is open, I can choose model for different products almost instantly. Is this normal?
I am trying to predict Revenue gererated by each Person.
My Input like this:
Month Person Revenue
-----------------------------------------
20050101 Person1 $1000
20050101 Person1 $2000
20050201 Person1 $1000
20050101 Person2 $5000
20050201 Person2 $2000
20050201 Person2 $3000
Obviosly for Person1 and 200501 I expect to see on MS Time Series Viewer $3000, correct?
Instead I see REVENUE(actual) - 200501 VALUE =XXX,
Where XXX is absolutly different number.
Also there are negative numbers in forecast area which is not correct form business point
Person1 who is tough guy tryed to shoot me.
What I am doing wrong. Could you please give me an idea how to extract correct
historical and predict information?
Thnak you,
Tim.
Hi Jamie:
I am building data mining models to predict the amount of data storage in GB we will need in the future based on what we have used in the past. I have a table for each device with the amount of storage on that device for each day going back one year. I am using the Time Series algorithm to build these mining models. In many cases, where the storage size does not change abruptly, the model is able to predict several periods forward. However, when there are abrupt changes in storage size (due to factors such as truncating transaction logs on the database ), the mining model will not predict more than two periods. Is there something I can change in terms of the parameters the Time Series Algorithm uses so that it can predict farther forward in time or is this the wrong Algorithm to deal with data patterns that have a saw tooth pattern with a negative linear component.
Thanks,
I probably posted my question in the wrong forum so please check here:
http://forums.microsoft.com/TechNet/ShowPost.aspx?PostID=3009605&SiteID=17
thanks
Hello,
I was working with Microsoft Time Series (MTS) algorithm and simulated data in order to evaluate/know it a little more.
I simulated 24 points of the model y[t] = 5.74-0.1486 y[t-1] + e[t] and 19 points of the model y[t] = 10.48-0.0486 y[t-1] + e[t] (a change of level), where e ~ N(0,0.01).
The MTS output is: if time>=23.5 then AR(3) else AR(1): y[t] = 6.23-0.2536 y[t-1].
So, I am wondering:
how the algorithm works whit the time variable as a split variable? Like the other variables? Only considering 4 time points?
Why the MTS algorithm produces AR(p) models where p is a little large (like the example: I simulated an AR(1) model and the output is an AR(3) model), what about parsimony models?
A AR model is a stationary model, so what happen if some data have trend? We need eliminate the trend before the MTS algorithm can be used?Thanks for your time
Hi, all experts here,
Thank you very much for your kind attention.
I am confused on key time column selection. e.g, I want to predict monthly sales amount, then what column in date dimension should I choose to be the key time column? Is it calendar_date (the key of date dimension) column or calendar_month?
Thanks a lot for your kind advices and help and I am looking forward to hearing from you shortly.
With best regards,
Yours sincerely,
hi to every one
and again i say my question
for the first time i thought if i ask my question from this forum i can give my answer exactly
but with these answers i see it was just a dream .
one person said you can see your answer in book with this title "the datamining with sql 2005 "but i cant find my request and then said you can find in datamning sql2005.com and in this site i cant find a sample about a form that i can show my result of prediftion with time series
i dont know how can i earn this code .and a sample about that .please please if every one can answer me ,answer with descripstion about code .i just want codes for connecting between c# and analysis serveice and a description about quality of this code,that means this code, how do it work?
Hi,
I'm trying to create a DM model using TS algorithm, to predict sales for different products and channels but I can only get it to work using one of those two "dimensions" or columns the other one is ignored (This is, my fact table contains a key for time, a key for channel a key for product and the metrics and the model only seems to allow working with time, the metrics and only one of the other dimensions product or channel ..) Am I missing something?
Thanks
Hi all,
I have a very simple time series model which processing works fine without any problem. However when I run the following query
SELECT
[TimeSeries].[PriceChange],
[TimeSeries].[Symbol],
PredictTimeSeries(PriceChange, -3, 2)
From
[TimeSeries]
WHERE
[TimeSeries].[Symbol] = 'x'
I get the following error:
TITLE: Microsoft SQL Server 2005 Analysis Services
------------------------------
Error (Data mining): A time series prediction was requested with a start time further in the past than the internal models of the mining model, TimeSeries, specified in the HISTORIC_MODEL_GAP and HISTORIC_MODEL_COUNT parameters can process.
The following is the excerpt of the minding model script related to the two parameters:
<AlgorithmParameters>
<AlgorithmParameter>
<Name>MISSING_VALUE_SUBSTITUTION</Name>
<Value xsi:type="xsdtring">Previous</Value>
</AlgorithmParameter>
<AlgorithmParameter>
<Name>HISTORIC_MODEL_GAP</Name>
<Value xsi:type="xsd:int">1</Value>
</AlgorithmParameter>
<AlgorithmParameter>
<Name>HISTORIC_MODEL_COUNT</Name>
<Value xsi:type="xsd:int">10</Value>
</AlgorithmParameter>
</AlgorithmParameters>
These HISTORIC_MODEL_GAP (1) and HISTORIC_MODEL_COUNT (10) should accommodate PredictTimeSeries(PriceChange, -3, 2). Could anyone shed some light on this?
i'm building a time series model in visual studio, forecasting sales. but it shows
The graph cannot be created because of the following error:
'All actual columns are ending in NULL: data unacceptable for viewer'. (Microsoft Visual Studio)
can anyone help solve this? the data is by month (for 3 years) and i want to forecast 3 months in the future.
thanks!
lxm
Hi, all here,
I want to hear from you for your experiences on how can we be convinced by what the model predicts particularly for Time Series algorithm? As we are not able to see the model accuracy chart for that, in this case, how can use the model results? E.g. we wanna know the possible sales amount of next month for a particular store in order to buy in the goods, in this case, how can we make the most of the prediction by the model? To shop ower, well, if the result is too far away from the usual sales amount, then it is unbelievable, thus, in this case, what else can we try? Keep training the models until its results sounds reasonable? Or what else can we try?Thanks in advance for your advices and help and I am looking forward to hearing from you shortly.
With best regards,
Yours sincerely,
Hi,
I have built a Time Series model to forecast Sales Value. I have used data form Jan 2005 to Sep2006 to train the model.
When
I write a DMX query to forecast the sales value for next 5 months, it is throwing me the output in the following format.
$TIME Sales Value
200610 2874
200611 2875
200612 2874
200613 2874
200614 2874
whereas the output i expected was.
$TIME Sales Value
200610 2874
200611 2875
200612 2874
200701 2874
200702 2874
What changes should i make in the model to get the output in the above format.?
Hi,
I have built a Sales Forecast model to predict the sales
value.
Along with making historic predictions for previous time
periods I also want to retrieve the actual sales values for those periods.
How can I achieve this in a time series model?
I also would like to know how do mining models store the data.
Do they store the data in the same table/view format
as their respecive data source view or in the Model Content format.
As the Microsoft Time Series algorithm implementation is based upon the Autoregressive Tree approach described in:
C. Meek, D. M. Chickering, D. Heckerman. Autoregressive Tree Models for Time-Series Analysis. In Proc. 2nd Intl. SIAM Conf. on Data Mining, 2002 (SDM-02). SIAM, pp. 229 €“ 244. http://www.siam.org/meetings/sdm02/proceedings/sdm02-14.pdf.
The model estimated is refererred to as an instance of "... autoregressive tree models of length p, denoted ART(p). An ART(p) model is an ART model in which each leaf node of the decision tree contains an AR(p) model, and the split variables for the decision tree are chosen from among the previous p variables in the time series..." (see the last paragraph of p. 2 of the paper).
What is the value of "p" used in the Microsoft Time Series implementation -- specifically, how many previous time series variables are used in estimating the model? It doesn't appear that this value can be specified in the algorithm parameters -- is that correct?
Thanks,
- Paul
Hi, all experts here,
Thanks for your kind attention.
I want to use time series algorithm to mine data from my case table and nested table. Case table is Date table, while nested table is the fact table. E.g, I want to predict the monthly sales amount for different region (I have region table related to the fact table), how can I achieve this?
Thanks a lot and I hope it is clear for your help and I am looking forward to hearing from you shortly.
With best regards,
Yours sincerely,
Hi, all experts here,
Thank you very much for your kind attention.
I am confused on key column of case table and key time column of nested table by using Time Series algorithm.
In my case, the case table structure is as below:
Territory key text (the ID is actually dimrisk_key, in this case, I use the name column binding to combine the Territory column of case table Dimrisks),
While the nested table structure is as below:
Cal_month key time (in this case, actually the ID is dimdate_key, again, I used name column bining property to bind the Cal_month to the ID)
So my question is, as the key column of case table has been set to be Territory, as a result, does the model training still cover all the cases (rows) based on the ID of the table?
Also, in the nested table, as the key time column has been set to Cal_month rather than Dimdate_key of the nested table, as a result, would the single series based on the cal_month?
Hope it is clear for your advices and help.
And I am looking forward to hearing from you shortly.
With best regards,
Yours sincerely,
Is it possible to group many time series into clusters by using the clustering algorithm of the SQL server 2005. The same question applies to "association rules" technique. Any examples?
Hi all,
I'm wondering what is the best way to store time-series data in an SQL database?? I've done a bit of investigating on the rrdtool (round robin database tool) that is used in a lot of nix based solutions for monitoring network equipment. I have a need to collect performance data from servers and routers and then produces some nice graphs from that data. I'm just not sure who i should store that data without the database growing to some huge size.
Any suggestions?
Hi,
I've got a time series of the number of new customer subscriptions, which
is the target attribute to predict. The number of subscriptions depends on
various marketing activities, such as mailings, which are known within
the time series for the past.
If I train an ART (MS Autoregression Tree), it learns the trend pattern
as well as the correlations between the marketing activities and the
target (cross-correlations), right?
What I would like to do is, providing the model with some marketing activity
planning for the future and let the model predict the number of new
subscriptions based on a) the past trend pattern and b) the future activities.
Unfortunately a time series algorithm does not provide some kind of scoring for input data.
What would be the best approach to solve this problem? How about linear regression?
But how to train a regression model with trend patterns?
Thanks for your help!
Hi, all,
Why I got different results for the same attribute value displayed in my mining model? Any suggestions on what I may have missed for that?
In my case, the mining structure is with case table which is the fact table, within this mining structure, I dragged other attributes from its related dimensional tables as well. E.g The schema of the mining structure is as below:
.........................................................................................
Fact events key (key of the case fact table)
Agent type (attribute dragged from its related dimension table, which is not the key attribute of that dimension table)
Agent level (attribute dragged from its related dimension table, and it is not the key attribute of that dimension table as well)
......and other attributes dragged from its related dimension tables.
.......................................................................................
Then in my training model (using Microsoft Clustering algorithm), the content contained within the training model is very strange, e.g there are different results for the same value of attribute 'Agent Level' . Why did that happen and how can I figure it out? There should only one result for each value of each attribute within one mining model?
Please shed me some light on this issue and I am looking forward to hearing from you shortly for your kind advices and thanks a lot in advance.
With best regards,
Yours sincerely,
The strange in this problem is that i got this problem suddenly and before all was running without any error.
The script that cause the error is in the trigger for insertion on a table, there is somewhere where i convert from varchar to bigint, but all seems fine to me i printed the varchar variable and the conversion looks valid, also i included several print statement in the script for debugging purposes and i feel everything is right and there shouldn't be any error.
I don't think anything changed in my db, so i wonder where this problem is comming from ?
this is the code:
Code Block
-- Private Sending start
declare @tmpmsg nvarchar(200)
set @tmpmsg = ltrim(rtrim(@message)) -- Remove leading and trailing blanks
if (left(@tmpmsg,4))='خاص' and len(@tmpmsg)>6
begin
declare @msg_error nvarchar(70)
declare @msg_error1 nvarchar(70)
declare @msg_error2 nvarchar(70)
declare @msg_error3 nvarchar(70)
declare @sender_id bigint
set @msg_error1 = 'الرجاء إرسال كلمة خاص متبوعة برقم ملÙ? الشخص المطلوب ثم الرسالة الشخصية'
set @msg_error2 = 'الرجاء تسجيل ملÙ?Ùƒ قبل إستخدام خدمة الرسائل الخاصة'
set @msg_error3 = 'إن ملÙ? الشخص المطلوب غير موجود'
-- The message should not go to chat filtration
update received_in set sent = 3 where reqid = @id
-- if the sender profile doesn't exist, send him a message indicating that.
SELECT @sender_id = profile_id FROM profiles where msisdn=@msisdn
if @sender_id is not null
begin
set @tmpmsg = ltrim(right(@tmpmsg, len(@tmpmsg)-3))
-- Now the message looks like '12345 this is a test message'
declare @profileid bigint
declare @strprofileid nvarchar(50)
declare @posSpace int
set @posSpace = charindex(' ', @tmpmsg)
if @posSpace = 0 -- Not found (the message format is like this 'pri 3432434 ' or 'pri dfsdffsd '
begin
set @msg_error = @msg_error1
-- Insert the message into the filtration screen to be fixed by a filtrator, the recipient_id will be set to 0
insert into mobile_chat (sender_id, recipient_id, message, datein, timein)
Values (@sender_id, 0, @tmpmsg, @thedate, @thetime)
end
else
begin
set @strprofileid = left(@tmpmsg, @posSpace-1)
-- first char separating profileid and util message
if isnumeric(@strprofileid) = 1
begin
set @profileid = @strprofileid
declare @utilmsg nvarchar(200)
set @utilmsg = ltrim(right(@tmpmsg, len(@tmpmsg)-@posSpace))
-- Right message format
print 'Profile id=' + ltrim(str(@profileid))
print 'Message=' + @utilmsg
declare @recmsisdn varchar(16)
select @recmsisdn = msisdn From profiles where profile_id=@profileid
-- if the receiver profile doens't exist, send him a message indicating that
if @recmsisdn is not null
begin
-- All fine: msg format, sender and receiver
if left(@recmsisdn,3)='000'
Insert into Inbox (sender_id, recipient_id, message, datein, timein)
Values (@sender_id, @profileid, @utilmsg, @thedate, @thetime)
else
insert into mobile_chat (sender_id, recipient_id, message, datein, timein)
Values (@sender_id, @profileid, @utilmsg, @thedate, @thetime)
end
else
begin
-- Insert the message into the filtration screen to be fixed by a filtrator, the recipient_id will be set to 0
insert into mobile_chat (sender_id, recipient_id, message, datein, timein)
Values (@sender_id, 0, @tmpmsg, @thedate, @thetime)
set @msg_error = @msg_error3
end
end
else
begin
set @msg_error = @msg_error1
end
end
end
else
-- The sender profile is not registered with us
set @msg_error = @msg_error2
if @msg_error is not null
begin
print @msg_error
if left(@msisdn, 3) = '966' -- We can only send replies to KSA mobiles
INSERT INTO cgi_sms_services.dbo.cgi_sms_in(msisdn, sms_code, message, reqport, smstype)
VALUES (@msisdn, '1000000002', @msg_error, 86367, 'wcsms')
end
end -- Private Sending End
Hello,
I was working with Microsoft Time Series model (MTS) with some data, when in the mining model viewer, decision tree tab, I realized that the key time variable that I define, it was acting like a split variable.
So, I ask you, this is possible?, because, for me, this should not happen€¦.
After, I review the Data Mining Tutorial by Seth Paul, Jamie MacLennan, Zhaohui Tang and Scott Oveson, and I found, in the Forecasting part, that the key time variable (Time Index) it was acting like a split variable too, in for example, M200 pacific:Quantity and R250 Europe:Quantity.
So people, it€™s possible that a key time variable act like a split variable in a MTS model?
Thanks
I am working on a stock price analysis project. Stock prices are stored in SQL Server as tick by tick data (that means in a high frequency) and need to be grouped by minutes in order to display them as as high, low, open and close prices in a candlestick or similar chart.
The fields in the table are:
Time (for every stock price tick)
Price (stock price)
Symbol (of the stock)
A sample time series would look like this:
11:45:01 $22.11 MSFT
11:45:02 $22.10 MSFT
11:45:04 $22.25 MSFT
...
...
...
11:45:58 $22.45 MSFT
11:46:03 $22.22 MSFT
11:46:08 $22.25 MSFT
...
...
...
11:46:59 $22.45 MSFT
The result of the SQL query should be:
Symbol Time Open High Low Close
MSFT 11:45 $22.11 $22.45 $22.10 $22.45
MSFT 11:46 $22.22 $22.45 $22.22 $22.45
This is the SQL statement I came up with but obviously doesn't work:
SELECT DATEPART(Minute,Time), MIN(Price), MAX(Price) FROM dbo.TimeSales WHERE Symbol='MSFT' GROUP BY DATEPART(Minute,Time)
I would appreciate any suggestions.
Thank you!
I m using the Time Series Algorithm to forecast sales across regions for various products. Assume the model is built with last 3 years data with the periodicity being monthly.
Is it possible that sometimes I can make predictions based on just 1 yr or 2 yrs data for certain products alone or certain regions alone? Can this be done without having to retrain the already built model?
Also, is it possible that using the model, i can predict week-wise / month-wise / quarterly sales as well?
Hi All,
I have a table Test1:
ID date Value
AAUGVAL 2/27/198760.848
AAUGVAL 3/2/1987 64.288
AAUGVAL 3/3/1987 63.77
AAUGVAL 3/4/1987 62.495
AAUGVAL 3/5/1987 62.65
AAUGVAL 3/6/1987 62.548
AAUGVAL 3/9/1987 62.292
AAUGVAL 3/10/198763.045
AAUGVAL 3/11/198763.021
....
I am trying to see the value % changes day by day and here is is the code I wrote:
select
starttime=cast(v.date as char(8)), endtime=cast(a.date as char(8)),
startval=v.Value, endval=a.Value,
change=substring('- +', sign((a.Value-v.Value)+2,1)+ cast(abs(a.Value-v.Value) as varchar)
from
(select date,Value, ranking =(select count(distinct date) from Test1 T where T.Value<=S.Value)
from Test1 S) v left outer join
(select date,Value, ranking=(select count(distinct date) from Test1 T where T.Value<=S.Value)
from Test1 S) a
on( a.ranking=v.ranking+1)
I got the following error message:
Server: Msg 174, Level 15, State 1, Line 4
The sign function requires 1 arguments.
Server: Msg 170, Level 15, State 1, Line 7
Line 7: Incorrect syntax near 'v'.
Server: Msg 170, Level 15, State 1, Line 9
Line 9: Incorrect syntax near 'a'.
Could someone please help with this? Thank you in advance!
shiparsons
I am trying to write a stored proc the calculates a moving average overthree periods. In the following example, I need to stratify the data bypersonID and RecordID in the #Temp table, but I am not sure how to doit. Right now I am restricting the data I use to build my time series bypersonID and I get the results I want *by PersonID*. If I can figure outhow stratify by personID so I don't have to use this restriction, I'msure I can extend it to the RecordID.Create Table #Temp(tmpID int identity,DetailID int,RecordID int,AdminDate Datetime,AdminTime datetime,Status tinyint,--decimal(9,2),Location varchar(100),PersonID char(9),PatientName varchar(100),DOB Datetime,Drug varchar(100),Sort varchar(10))--populate with data by personIDinsert into#Temp(DetailID,RecordID,AdminDate,AdminTime,Status ,Location,PersonID,PatientName,DOB,Drug,Sort)Select MD.PatMedOrderDetailID, MD.PatMedOrderID, M.Date as AdminDate,Case M.Time When 'A' then '8:00:00 AM' When 'N' then '12:00:00 AM' When'P' then '4:00:00 AM'When 'H' then '8:00:00 PM' else M.Time End as Admintime,100*M.Status, P.Location,P.PersonID, P.Name as PatientName, P.DOB,D.GenericName + ' (' + D.TradeName + ') ' +D.Strength,Left(P.Location,3)From PatMedOrderDetail MD Inner Join PatMedOrder MO on MD.PatMedOrderID= MO.PatMedOrderIDinner Join PatMedPass M on MD.PatMedOrderDetailID =M.PatMedOrderDetailIDinner join Patient P on M.PersonID = P.PersonIDinner join Drugs D on MO.DrugID = D.DrugIDWhere P.PersonID = '000126230'Order by P.PersonID,MD.patMedorderID, M.Date, M.TimeSelect * from #Temp -- to view entire set--returns relevant rowsSelect Derived.RefusalRate,T.* from #Temp T inner join(select t1.tmpID, avg(t2.Status) as RefusalRatefrom #Temp t1 cross join #Temp t2WHERE t1.tmpID>=3 AND t1.tmpID BETWEEN t2.tmpID AND t2.tmpID+2group by T1.tmpIDhaving avg(t2.Status)< 100) as Derived on T.tmpID = Derived.tmpIDDrop Table #Temp*** Sent via Developersdex http://www.developersdex.com ***
View 1 Replies View RelatedGiven the following table information:HOSTNAME DATETIMEWEBNYC001 2005-06-15 10:30AMWEBNYC001 2005-06-15 10:31AMWEBNYC001 2005-06-15 10:31AMWEBNYC001 2005-06-15 10:34AMWEBNYC001 2005-06-15 10:35AMWEBNYC001 2005-06-15 10:35AMWEBNYC002 2005-06-15 10:30AMWEBNYC002 2005-06-15 10:30AMWEBNYC002 2005-06-15 10:33AMWEBNYC002 2005-06-15 10:35AMWEBNYC002 2005-06-15 10:35AMWEBNYC002 2005-06-15 10:35AMHow can I easily return the following results:HOSTNAME DATETIME COUNTWEBNYC001 2005-06-15 10:30AM 1WEBNYC001 2005-06-15 10:31AM 2WEBNYC001 2005-06-15 10:32AM 0WEBNYC001 2005-06-15 10:33AM 0WEBNYC001 2005-06-15 10:34AM 1WEBNYC001 2005-06-15 10:35AM 2WEBNYC002 2005-06-15 10:30AM 2WEBNYC002 2005-06-15 10:31AM 0WEBNYC002 2005-06-15 10:32AM 0WEBNYC002 2005-06-15 10:33AM 1WEBNYC002 2005-06-15 10:34AM 0WEBNYC002 2005-06-15 10:35AM 3Thanks!
View 2 Replies View RelatedHello,
I am new to SQL Server and learning lots very quickly! I am experienced at building databases in Access and using VBA in Access and Excel.
I have a time series of 1440 records that may have some gaps in it. I need to check the time series for gaps and then fill these or reject the time series.
The criteria for accepting and rejecting is a user defined number of time steps from 1 to 10. For example, if the user sets the maximum gap as 5 time steps and a gap has 5 or less then I simply want to lineraly interpolate betwen the two timesteps bounding the gap. If the gap is 6 time steps then I will reject the timeseries.
I have searched the BOL and MSDN for SQL Server and think there must be a solution using the PredictTimeSeries in DMX, but not quite sure if I can do this. I may be better off simply passing through the time series as a recordset and processing as I would have done in Access...(I am reluctant to do this as I have of the order 100 * 5 * 365 time series and growng by 100 each day and fear it will take quite some time...)
Can anyone help me by pointing me in the right direction please?
Unless there is a way of using PredictTimeSeries on its own, I think the solution is:
Identify if a record is the a valid one or part of a gap (ie missing values).
Identify the longest gap and reject or process data on this value.
Identify if a record preceedes or succeeds a gap.
For each gap fill it using a linear interpolation.
Thanks,
Alan.
Hi!
First question: How many months of data do you need to make this algorithm to work in Excel 2007? And is there an issue about data types in Excel for this algorithm?
I have found some odd behaviours regarding this. If I use the DM sample Excel 2007 with time series data everything works fine. If I copy and paste data into Excel 2007, from another data source, I can get a forecast of repeating values, that is one value, that will be repeated for each month that I am trying to do a forecast.
Should I avoid having time members for forecast dates in a column? Sometimes my forecast values will be placed below my dates that do not have values. If I am forecasting months in 2008, with month values from 2007 and 2006 the forecast values will be placed below my 2008 empty months.
Kind Regards
Thomas Ivarsson
I'm trying to learn about time series algorithm but I can't set the time periodicity right. I have information stored 2 times a year (semester) so I'll should set up a PERIODICITY_HINT = {2}, right? but it does not change anything.
Here is a screenshot that might help understand the problem:
I'm going to create an analysis report based on time range. The data is grouped by the hourly range. There're two problems that I'm facing.
1. How can I generate such result set so that it will give me 0 count instead of missing that column?
2. How can I vary the start and end time which depends on another table?
I believe this is quite hard to be complete within a single SQL. However, I would still want to try. The SQL server is the Express version. No analysis service is available.