05-26-2021
mvgilliland
SAS Employee
Member since
02-07-2012
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Latest posts by mvgilliland
Subject Views Posted 4168 07-01-2020 03:01 PM 1990 11-21-2019 11:10 AM 735 08-12-2019 11:18 AM 1069 02-14-2018 12:05 PM 2114 01-24-2018 07:51 AM 1906 01-17-2018 01:05 PM 3812 07-27-2017 11:15 AM 4009 02-03-2017 10:47 AM 1759 09-23-2016 10:03 AM 1963 08-31-2016 09:47 AM -
Activity Feed for mvgilliland
- Posted Re: Super Bowl 2021: Will the date be changed? on SAS Communities Library. 07-01-2020 03:01 PM
- Got a Like for Announcing 2019 SAS/IIF Forecasting Research Grants. 11-23-2019 02:09 PM
- Got a Like for SAS Visual Forecasting 8.5 is now available!. 11-23-2019 02:07 PM
- Got a Like for SAS Visual Forecasting 8.5 is now available!. 11-22-2019 10:27 AM
- Posted SAS Visual Forecasting 8.5 is now available! on SAS Forecasting and Econometrics. 11-21-2019 11:10 AM
- Posted Announcing 2019 SAS/IIF Forecasting Research Grants on SAS Forecasting and Econometrics. 08-12-2019 11:18 AM
- Got a Like for Two new articles in Journal of Business Forecasting. 02-14-2018 12:10 PM
- Posted Two new articles in Journal of Business Forecasting on SAS Forecasting and Econometrics. 02-14-2018 12:05 PM
- Posted SAS Econometrics 8.2 is now available! on SAS Communities Library. 01-24-2018 07:51 AM
- Posted SAS Visual Forecasting 8.2 is now available! on SAS Communities Library. 01-17-2018 01:05 PM
- Posted Access to World Weather and NOAA Severe Weather Inventory Databases on SAS Communities Library. 07-27-2017 11:15 AM
- Posted Automated Forecasting and FVA on SAS Communities Library. 02-03-2017 10:47 AM
- Posted Re: SAS Forecast Studio on SAS Forecasting and Econometrics. 09-23-2016 10:03 AM
- Posted Re: Questions about convergence in Proc Model on SAS Forecasting and Econometrics. 08-31-2016 09:47 AM
- Got a Like for 2016 SAS/IIF Grant to Promote Research on Forecasting. 07-08-2016 07:13 AM
- Posted 2016 SAS/IIF Grant to Promote Research on Forecasting on SAS Forecasting and Econometrics. 07-07-2016 04:05 PM
- Posted Re: SAS Usage into Housing on SAS Forecasting and Econometrics. 07-06-2016 04:36 PM
- Got a Like for Re: Out-of-Sample Forecasting with Unobserved Components Model. 04-11-2016 12:03 PM
- Posted Re: Out-of-Sample Forecasting with Unobserved Components Model on SAS Forecasting and Econometrics. 04-11-2016 10:47 AM
- Got a Like for Re: Forecast for daily using week parameter in Forecast Studio. 09-01-2015 04:24 AM
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My Liked Posts
Subject Likes Posted 1 08-12-2019 11:18 AM 2 11-21-2019 11:10 AM 1 02-14-2018 12:05 PM 1 07-07-2016 04:05 PM 1 04-11-2016 10:47 AM -
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07-01-2020
03:01 PM
1 Like
A practical example, clearly explained -- thanks Tammy!
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11-21-2019
11:10 AM
2 Likes
SAS Visual Forecasting 8.5 (on SAS Viya 3.5) is now available, with several new capabilities including:
Configurable forecasting view within the forecasting nodes
Nested facets in facet panel
Improved filtering capabilities
New Time Series Dimension Reduction (TDR) package
New External Language (EXTLANG) package provides support for open-source code (Python and R)
Find full details on the SAS Visual Forecasting product page.
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08-12-2019
11:18 AM
1 Like
The International Institute of Forecasters and SAS® announce two $10,000 grants to support research on forecasting. Per the announcement:
Forecasting research has seen major changes in the theoretical ideas underpinning forecasting effectiveness over the last 30 years. However, there has been less impact on forecasting practice. We aim to put this right. For the sixteenth year, the IIF, in collaboration with SAS, is proud to announce financial support for research on how to improve forecasting methods, and business forecasting practice, including organisational aspects of management of the forecasting process. This year’s support will consist of a total of two $10,000 grants in methodology and practice/management categories.
Applications should be submitted to the IIF Office by September 30, 2019. The application must include:
• Description of the project (at most 4 pages) • C.V./resume (brief, 4 page max) • Budget and work-plan for the project (brief, 1 page max)
Criteria for the award of the grant will include likely impact on forecasting methods and business applications.
Consideration will be given to new researchers, and whether supplementary funding is likely to be gained. It is also expected that the research supported by the SAS/IIF grant be presented in an International Symposium on Forecasting (ISF) organized by the IIF. The applications will be assessed through a committee appointed by the IIF directors. The results of the evaluation will be announced to the applicants within 12 weeks of the closing date.
Grant recipients are also required to author a paper reporting on their research for possible publication in the International Journal of Forecasting (IJF). Therefore it is useful to keep in mind the IJF scope.
In addition to the advances made at our academic research institutions, there is also considerable innovation coming from industry practitioners, who are encouraged to submit proposals.
Find more details about the application process, and learn about previous recipients, on the IIF website.
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02-14-2018
12:05 PM
1 Like
The Winter 2017-2018 Journal of Business Forecasting is a special issue on the future of demand planning & forecasting. It includes two articles by SAS authors:
Charlie Chase, "Real-Time Demand Execution Anticipating Demand at the Edge"
Executive Summary: Using real-time information as it is streaming in from connected devices on the Internet of Things (IoT), edge analytics is gaining attention as the IoT has become more widespread, streaming data from manufacturing machines, online purchases, and mobile and other remote devices. Using analytic algorithms as data are generated, at the edge of the corporate network, companies can set constraints to determine what information is worth sending to the Cloud, to a demand signal repository, or other data repositories for later use. The key benefit of edge analytics is the ability to analyze data as they are generated, which decreases latency in the decision-making process as the data are collected. Rather than designing consolidated data systems where all the data are sent back to an enterprise data warehouse (or data lake) in a raw state, where they have to be cleaned and analyzed before being of any value, why not do everything at the edge of the system, including demand forecasting, using advanced algorithms or machine learning? Are you stuck in a vicious cycle of planning demand using two-to-four-week-old data, or are you conducting real-time demand execution anticipating demand at the edge?
Mike Gilliland, "The Move to Defensive Business Forecasting"
Executive Summary: Despite continuing technological advances that take us to the limits of achievable accuracy, most companies still struggle with forecasting, with many forecasts less accurate than a naive model. More complex statistical modeling, by itself, does not provide the answer. Instead, significant performance improvement can come from a "defensive" approach to business forecasting, using tools like FVA analysis. With a defensive approach, organizations can identify the waste and bad practices that degrade forecasting performance, and can thereby achieve the full potential of the technological advances.
The full articles are available to JBF subscribers and Institute of Business Forecasting members.
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01-24-2018
07:51 AM
For 40 years SAS has delivered the cutting edge of econometrics and time series analysis through SAS/ETS, which now has over 8000 licenses worldwide.
In March 2017, we expanded our footprint with the release of SAS Econometrics 8.1, taking advantage of Viya, leveraging the speed, scalability, and elasticity of the SAS in-memory environment. This initial release included five procedures:
CCOPULA simulates multivariate distributions for copula models.
CNTSELECT does regression models for integer valued dependent variables.
CPANEL fits linear regression models for panel data.
CQLIM does regression models for qualitative and limited dependent variables.
SEVSELECT fits distributions to the size or severity of losses or other events.
Now, SAS Econometrics 8.2 adds enhancements to the original procedures, along with four new procedures:
CCDM estimates a compound distribution model.
CSPATIALREG estimates linear spatial econometric models for cross-sectional data whose observations are spatially referenced.
HMM estimates hidden Markov models (HMMs) where the observables are independently distributed, conditional on the hidden states that follow a Markov chain.
TSMODEL provides a new environment for time series modeling and cloud computing.
Also, SAS Econometrics now lets you utilize SAS/ETS procedures through SAS Studio.
SAS Econometrics equips our customers to address difficult, real-life questions by providing techniques to model complex business and economic scenarios and analyze the dynamic impact that specific events might have over time. It can help you understand the impact that factors such as economic and market conditions, customer demographics, pricing decisions and marketing activity have on your business, providing a scientific basis for better decision making.
SAS Econometrics is designed for all analytic audiences, including economists, financial markets analysts, data scientists, forecasters, and business analysts – anyone who deals with business, economic or time series data.
For more information, see the SAS Econometrics product page and documentation.
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01-17-2018
01:05 PM
2 Likes
In March 2017 we released SAS Visual Forecasting 8.1, built for Viya, and introduced an open platform – a forecasting ecosystem. SAS Visual Forecasting was designed to be highly scalable, making one pass through the data to do multiple analyses.
Then in December, we upped the game with the release of SAS Visual Forecasting 8.2.
The biggest news of this new release is that SAS Visual Forecasting now has a user interface. The interface facilitates a process workflow approach where you can organize and structure your forecasting process.
Within your workflow you can explore and prepare data, and utilize SAS supplied or custom forecast models that you have created. The interface allows you to compare and visualize forecast results, and make overrides to the statistical forecasts.
Also new, SAS Visual Forecasting 8.2 adds an extremely flexible capability for making manual overrides to the statistical forecast generated by your models. Instead of being limited to overrides within the forecasting project hierarchy, you can now make overrides based on attributes – such as a product’s color, or customer sentiment from an analysis of online reviews.
At the core of SAS Visual Forecasting is the TSMODEL procedure. TSMODEL innovatively requires just one read of the data to do multiple analyses. Its scripting language is optimized and compiled based on the machine it is running on, and the scripts are distributed to the available hardware.
In 8.2, two packages are added to the TSMODEL procedure.
The SSA package supports Singular Spectrum Analysis, a powerful tool for detecting patterns in long time series with few model assumptions. Spectral groupings can then be individually analyzed using time series techniques.
The MOTIF package supports Motif Analysis, for the discovery and detection of motifs in time series data.
The new TSINFO procedure handles date variables in the input data, and can automatically determine the suitability of time ID variables in an input data set.
And finally, SAS Visual Forecasting 8.2 lets you run both SAS/ETS procedures and the SAS Forecast Server Procedures (in addition to its Viya procedures) through SAS Studio.
For more information, see the SAS Visual Forecasting product page and documentation.
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07-27-2017
11:15 AM
7 Likes
Forecasters and econometricians need access to data, and SAS/ETS has long provided easy access to many commercial and government databases, including:
Commercial database vendors: FAME, DRI, Standard & Poor’s (COMPUSTAT), FactSet, Haver Analytics DLX and CRSP.
Federal Reserve Economic Data (FRED).
US government data: Bureau of Economic Analysis, Bureau of Labor Statistics.
International agency data: International Monetary Fund (IMF), Organization for Economic Cooperation and Development (OECD).
SAS/ACCESS® interfaces and SAS Data Surveyors (licensed separately) provide seamless read, write and update access to other data sources.
Since the latest 2016 release, SAS/ETS now includes access to two sources of weather-related data:
World Weather Online.
NOAA Severe Weather Data Inventory (SWDI) web service.
The SAS/ETS data access interface LIBNAME engines (SASERAIN and SASENOAA) provide seamless, efficient access to weather events and weather time series data supplied by World Weather Online and the NOAA Severe Weather Data Inventory web services.
The SASERAIN interface LIBNAME engine includes the following features:
enables SAS users to retrieve weather data from the World Weather Online website
uses the LIBNAME statement to enable you to download World Weather Online data and to specify which weather data time series you want to retrieve based on up to nine locations
works with the SAS DATA step to write the selected weather data to a SAS data set
selects past weather data based on a date range within 60 days prior to today for free nonpremium data (for premium data, the range must start no earlier than July 1, 2008)
selects local forecast data based on a range defined by number of days (starts today), premium returns up to 15 days, and nonpremium (free) returns up to 5 days
enables you to select the frequency of data, whether daily, hourly, every three hours, or otherwise
maintains the sort order, so the locations (q-codes) are sorted in the resulting SAS data set by the order specified in the QUERY= option, by date (time ID), and by variable (time series item name)
works with the SAS DATA step to perform further subsetting and to store weather data in a SAS data set
supports TLS connectivity by obtaining a secure connection using the CONNECT method (if necessary) and a PROXY
creates an XML map of the data for dynamic, flexible association of SAS formats and informats for all variables
The SASENOAA interface LIBNAME engine includes the following features:
enables SAS users to access severe weather data sets, such as those for tornado vortex signatures (NX3TVS), storm cell structure (NX3STRUCTURE), and preliminary local storm reports (PLSR)
works with the SAS DATA step to write the selected NOAA data to a SAS data set
selects data based on geospatial limits, such as by a bounding box or a centerpoint-radius combination
selects data based on a date range
returns data in these formats:
XML; data are returned in XML format
KMZ; data are returned in zipped KML format for Google My Maps (plot data on a map)
SHP; mapping data are returned in zipped Esri format (four files returned inside ZIP file)
works with the SAS DATA step to perform further subsetting and to store the resulting time series in a SAS data set
supports TLS connectivity by obtaining a secure connection using the CONNECT method (if necessary) and a PROXY
creates an XML map of the data for dynamic, flexible association of SAS formats and informats for all variables
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02-03-2017
10:47 AM
To properly evaluate (and improve) forecasting performance, we recommend our customers use a methodology called Forecast Value Added (FVA) analysis. FVA lets you identify forecasting process waste (activities that are failing to improve the forecast, or are even making it worse). The objective is to help the organization generate forecasts that are as accurate as can reasonably be expected (given the nature of what they are forecasting), and do this as efficiently as possible (using the fewest resources).
In its simplest form, FVA compares the accuracy of a Naive forecast to the organization’s current forecasting method (usually some form of statistical forecasting with manual adjustments, or even an entirely manual process). FVA can also compare performance to alternative methods, like an automated statistical forecast (which can be generated from software such as SAS® Forecast Server or SAS® Forecasting for Desktop).
Naïve forecast: The “no change” model is standard, where the forecast is that there will be no change from the latest observation. If the organization has a supply lead time, such as two months, then the “no change” forecast for a particular month will be the actual observed value from two months prior. So if 100 units were sold in November, the forecast for January is 100. If 125 are sold in December, then the February forecast is 125, and so on. For highly seasonal data, you may instead use a seasonal "no change" from the corresponding period the prior year. So the forecast for week 5 of 2017 is the actual from week 5 of 2016.
Manual forecast: Some organizations do not use forecasting software, and use an entirely manual process where the forecast is based on management judgment.
Statistical forecast: Generated by statistical models built by forecasters in forecasting software, based on historical sales alone, or by including additional variables (like pricing, promotions, events, etc.).
Automated forecast: Generated entirely automatically by forecasting software with no human intervention (no tuning the models, and no manually adjusting the forecasts).
In the latter two cases, there is often the option to make manual adjustments to the computer generated forecast.
The accuracy of the Naïve forecast serves as a basis for comparison against all other forecasting activities. The Naïve forecast can be created automatically at virtually no cost, so it is important for the organization to understand what accuracy the Naïve forecast can be expected to achieve. If the Naïve’s accuracy is “good enough” for the organization’s planning and decision making purposes, then it makes sense to just use it, and stop doing any manual or statistical forecasting efforts. (Why spend time and money on a costly forecasting process if the Naïve can generate “good enough” forecasts for free?)
In most situations, however, the organization seeks forecasts that are more accurate than what the Naïve can achieve. But a typical forecasting process, even when aided by statistical forecasting software, can consume a lot of management time, at considerable cost.
The FVA approach lets you focus your improvement efforts in areas most in need of improvement – such as those where your accuracy is worse than the Naïve forecast.
Many companies find that overall, their forecasting process is performing better than just using the Naïve forecast. However, there are usually specific areas (products, locations, or other points in the organization’s forecasting hierarchy) where the Naïve forecast performs better. These should be investigated, to see if there are explainable reasons why the forecast is worse than the Naïve, and whether it can be improved. (Often the cause of such poor forecasting is political pressures within the organization. The forecast represents what management wants to happen, rather than being an unbiased best guess of what really will happen.)
Upon doing FVA analysis, a surprisingly large number of companies find that overall they are forecasting worse than doing nothing and just using the Naïve forecast! If the forecasting process cannot be improved in these areas, then it is simply wasted effort, and should be eliminated in favor of using the Naïve.
Organizations often find that, overall, Automated forecasts are more accurate than what their current forecasting process produces. However, there will likely be some areas where the Automated forecast is less accurate than the existing process, or even less accurate than the Naïve forecast. Once these areas are identified, they can be investigated. (It is important to note that for some sales patterns, the naïve “no change” model is the most appropriate forecasting model, and cannot be meaningfully improved upon.)
To make it easy to identify non-value adding areas, you can build a simple application using SAS® Visual Analytics software. Such an application lets you point and click your way through the organization’s forecasting hierarchy, and at each point view performance of the Naïve, Manual, Statistical, and Automated forecasts (or whatever different types of forecasting methods you are using). This makes it very quick and easy to identify where forecasting methods are not adding value, so these areas can be investigated. SAS Visual Analytics provides a much better environment for doing this kind of analysis than trying to do it in Excel.
The value of an automated (or largely automated) forecasting process is threefold:
It can significantly reduce the amount of time spent on manual forecasting. This frees those resources currently engaged in forecasting to spend more time on sales and customer service activities that can increase revenue and customer satisfaction. Analysts can focus their efforts on the most important, or most problematic forecasts, and let the rest run on auto-pilot.
Automated forecasting will generally create more accurate forecasts. In many areas accuracy will be significantly better than the Naïve forecast, although sometimes accuracy will just be comparable to the Naïve. In areas where Manual forecasting does well, because the forecasters have information not available (or not suitable) for computer modeling, then manual adjustments can still be made to the statistical forecast. But automatic forecasting greatly reduces the need for manual efforts – only requiring them in areas where we have determined they are adding value.
Automated forecasting should create unbiased forecasts. Manual forecasting processes often have significant bias to over-forecast (forecasts higher than the actual sales turn out to be, due to wishful thinking, or to drive higher inventory and not leave unfilled orders). This adds costs in excess / obsolete capacity and supply. Some organizations have a bias to under-forecast, which might lower inventory costs, at the risk of poor customer service. Automated statistical forecasts should be virtually unbiased – neither chronically too low or too high compared to the actual sales.
In large-scale forecasting situations, automated forecasting is not just an option, it may be a necessity. It is not uncommon for a retailer, having thousands of items sold at hundreds of stores, to have over a million store/item forecasts they want to create. But no company, in any industry, can afford the army of analysts needed to manually build a million+ forecasting models.
When you combine FVA analysis with the large-scale automated forecasting capabilities in SAS Forecast Server (and larger-scale capabilities when you also utilize SAS® Grid Manager), even the largest enterprise can efficiently generate quality forecasts, and focus analyst efforts where they are most needed.
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09-23-2016
10:03 AM
Hi Lokendra,
Regarding 1., if product sales are either highly stable, or highly erratic, then a flat line (constant) forecast can be most appropriate.
For highly stable demand this intuitively makes sense, and would be easy to explain to a client or other user of the forecast.
In your situation, since this is retail data, I suspect you are seeing highly erratic demand patterns (particularly if you are forecasting at a granular level like store/item/week).
A flat line forecast may not make intuitive sense for highly erratic demand because it doesn't appear to "fit" the history. However, if there is no underlying structure in the data -- that what you have are essentially random ups and downs -- then there is no pattern to be fit, and just forecasting the average demand makes sense. This appears to be the situation with your data because the constant forecast is performing best in the holdout sample.
A 2015 article in the Journal of Business Research ("Simple versus Complex Forecasting: The Evidence" by Green and Armstrong) found considerable evidence that simple models forecast better than complex models. Complex models often "overfit" the history -- mimicking the randomness rather than any real structure in the data and projecting it forward -- and thereby generate less accurate forecasts.
--Mike
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08-31-2016
09:47 AM
You might be able to find the details you need in the PROC MODEL documentation: https://support.sas.com/documentation/cdl/en/etsug/68148/HTML/default/viewer.htm#etsug_model_toc.htm
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07-07-2016
04:05 PM
1 Like
For the fourteenth year, the International Institute of Forecasters, in collaboration with SAS®, is proud to announce financial support for research on how to improve forecasting methods and business forecasting practice. The award for the 2016-2017 year will be two $5,000 grants, in Business Applications and Methodology.
Criteria for the award of the grant will include likely impact on forecasting methods and business applications. Consideration will be given to new researchers in the field and whether supplementary funding is possible.
Applications must include:
Description of the project (max. 4 pages)
Letter of support from the home institution where the researcher is based.
Brief (max. 4 page) c.v.
Budget and work-plan for the project.
The deadline for applications is September 30, 2016. For a complete overview of the requirements for the award, click here.
All applications or inquiries should be sent to IIF Business Director (pamstroud@forecasters.org).
The IIF-SAS grant was created in 2002 by the IIF, with financial support from the SAS Institute, in order to promote research on forecasting principles and practice. The fund provided amounts of US $10,000 per year, which is divided to support research in the two basic aspects of forecasting: development of theoretical results and new methods, and practical applications with real-world comparisons.
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07-06-2016
04:36 PM
SAS Forecast Server is most appropriate if you have lots of time series to forecast (thousands +). For automatic forecasting with a GUI, but on a smaller scale, SAS Forecasting for Desktop is a cost effective option. Many customers also use SAS/ETS for econometric analysis and to build time series forecasting models.
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04-11-2016
10:47 AM
1 Like
It is generally preferable to utilize a hold out (or "test") sample, but of course, you don't always have as many historical data points as you'd like.
Rob Hyndman provides useful guidance on sizing your "training" and "test" data sets in the article "Measuring Forecast Accuracy" that appears in the new book Business Forecasting: Practical Problems and Solutions (Wiley, 2015). You can also check out his online text at www.otexts.org/fpp/2/5.
Per Hyndman, test data is typically about 20% of the total sample, and ideally is at least as large as the maximum forecast horizon required. So if you have monthly data and need to forecast out one year, you have plenty of observations to include 42 points in your training data and the most recent 12 points in your test data. This should work fine for monthly data since you have over 3 full year cycles in the training data
If you have weekly or daily data, you might want to use the method of time series cross-validation, that is described in the article (and also discussed in this blog post: http://blogs.sas.com/content/forecasting/2016/03/18/rob-hyndman-measuring-forecast-accuracy/
Time series cross-validation is a good method when you don't have enough historical data, and can't afford to split off an adequately sized test set. Udo Sglavo wrote about implementing time series cross-validation in SAS in this series of blog posts:
http://blogs.sas.com/content/forecasting/2011/09/01/come-on-irene-cross-validation-using-sas-forecast-server/
http://blogs.sas.com/content/forecasting/2011/09/02/guest-blogger-udo-sglavo-on-cross-validation-using-sas-forecast-server-part-1-of-2/
http://blogs.sas.com/content/forecasting/2011/09/06/guest-blogger-udo-sglavo-on-cross-validation-using-sas-forecast-server-part-2-of-2/
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06-16-2015
03:38 PM
For the thirteenth year, the IIF, in collaboration with SAS®, is proud to announce financial support for research on how to improve forecasting methods and business forecasting practice. The award for the 2015-2016 year will be (2) $5,000 grants. The deadline for applications is September 30, 2015. For the full description and requirements for the award, click here.
Applications must include:
Description of the project (max. 4 pages)
Letter of support from the home institution where the researcher is based.
Brief (max. 4 page) c.v.
Budget and work-plan for the project.
All applications or inquiries should be sent to IIF Business Director (forecasters@forecasters.org).
The IIF-SAS grant was created in 2002 by the IIF, with financial support from the SAS® Institute, in order to promote research on forecasting principles and practice. The fund provided amounts of US $10,000 per year, which is divided to support research in the two basic aspects of forecasting: development of theoretical results and new methods, and practical applications with real-world comparisons.
Full details are available at: Grants and Research Awards | International Institute of Forecasters
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05-19-2015
11:13 AM
3 Likes
Hi Naresh, If your "months" are based on some specified aggregation of weeks (such as 4/4/5 weeks per month in the 3 month quarter), you could just aggregate your weekly forecasts accordingly. If your "months" are actual calendar months, then there is no sure way to map the weekly forecasts to monthly. In that case one option is to split the weekly forecasts into days (you'll have to figure out how best to do that), and then aggregate to calendar month. Or simply aggregate your historical data by calendar month and start forecasting in monthly buckets. Or, if you are satisfied with the quality of the weekly forecasts, perhaps you can convince your management that they should be using weekly forecasts instead of monthly. I don't know your industry, but in most supply chain situations, forecasting and planning in weekly buckets is most appropriate. --Mike
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