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01-03-2018 12:06 PM

Hi

I have daily time series data from 2014 to today. I used it to build ARIMA models and forecast for the next days and weeks.

Now, i also have 15 minutes interval data, and would like to build model and do forecasting for the next 15 minutes periods.

I know that ARIMA model does not fit for interval forecasting. Do you have a model that fit for interval forecasting ?

Thanks

M. Pham

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Posted in reply to Phamhhm

01-03-2018 03:08 PM

Hi. do you have an example of your code? I am not sure why you cannot do the same forecasting with ARIMA that you did with the daily series. Could you do this by changing the seasonality of your interval?

I'm not sure what you mean by fitting for interval forecasting, isn't that what you did when you forecasted the daily data?

Jennifer

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Posted in reply to Jennifer_beeman_sas_com

01-03-2018 03:20 PM

We can use ARIMA for 15 minutes period, for daily, for monthly, ...

But ARIMA is not the best model for 15 minutes period.

I will figure out the appropriate model.

Thanks

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## Re: Time series

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Check out the time series and forecasting tasks in SAS Studio! They provide an easy point-and-click interface for Time Series Data Preparation, Time Series Exploration, and Time Series Modeling and Forecasting.

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Posted in reply to Phamhhm

01-03-2018 03:32 PM

It may help to know what is the nature of the data that you are trying to forecasts and why you think ARIMA is not adequate. SAS/ETS offers several other model classes for time series data. However, without knowledge of the data it is difficult to make suggestions.

Check out the time series and forecasting tasks in SAS Studio! They provide an easy point-and-click interface for Time Series Data Preparation, Time Series Exploration, and Time Series Modeling and Forecasting.

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Posted in reply to mitrov

01-03-2018 06:43 PM

Hi

Let me fix something and i will provide the data next week. So you can help me to find the best model.

Thanks

Pham

Let me fix something and i will provide the data next week. So you can help me to find the best model.

Thanks

Pham

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Posted in reply to Phamhhm

01-03-2018 06:53 PM

Hi,

How your current ARIMA models are performing with daily, weekly intervals?

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Posted in reply to stat_sas

01-03-2018 10:32 PM

As long as your time series measurements are equally spaced in time, you can use PROC ARIMA to model/forecast them. The time interval information such as daily, monthly, or fifteen-minute plays no role in the actual computation. The time interval information does play a role in what type of model you might want to fit the series. For example, monthly data usually has seasonal pattern of length 12 (differencing order of 12) whereas quarterly data has seasonal length of 4. This means that what model to fit your series depends on your knowledge about the series. You can specify very general ARIMA models using PROC ARIMA. You will need to decide what type of seasonality makes sense for your 15-minute interval series---for example, if your measurements are stock trades every fifteen minute during an 8 hour business day then your season length might be 32 (4 fifteen minute intervals per hour).

Hope this helps.

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Posted in reply to rselukar

01-03-2018 10:59 PM

Thanks,

Exactly, data from 8:30 to 16:30, (8hrs x 4 =32) for call center forecasting incoming calls.

I will try it at work, and let you know .

I have read papers from the topic, and they talk about different models that are not easy to write programs and need advanced statistics .

Exactly, data from 8:30 to 16:30, (8hrs x 4 =32) for call center forecasting incoming calls.

I will try it at work, and let you know .

I have read papers from the topic, and they talk about different models that are not easy to write programs and need advanced statistics .

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Posted in reply to Phamhhm

01-03-2018 11:07 PM

Good. In addition to ARIMA modeling, you can also consider UCM models. See "Example 42.3 Modeling Long Seasonal Patterns" in the PROC UCM doc.

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Posted in reply to Phamhhm

01-09-2018 09:24 AM

I think you probably need to include multiple seasonality in the model such as hour of the day and day of the week. You will need to create dummy vars or trig curves to represent the desired seasonality, and include them as independent variables in the model. You could using regression with some lagged independent variables or auto-regression in addition to ARIMA and UCM with the additional independent vars.

thanks

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Posted in reply to alexchien

01-09-2018 09:35 AM

Thanks for your advice, I will try .

Pham

Pham