Forecasting: Principles & Practice. Leader: Rob J Hyndman. September University of Western Australia tauneagmaivise.cf Resources. Slides. Exercises. Textbook. Useful links tauneagmaivise.cf . Forecasting: principles and practice. Background. 3. Forecasting: Principles and Practice - 2nd Edition. by Rob J Hyndman and George Athanasopoulos (PDF, Online reading) – 12 Chapters,
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This book claims to be about principles and practices. Most of the prin Today, however cfm The Power of Now: A Guide to Spiritual Enlightenment. Forecasting: principles and practice by Rob J Hyndman (Author), George book's notes: tauneagmaivise.cf Forecasting Principles and Practice - Rob Hyndman - Ebook download as PDF File .pdf) or read book online. Book about statistical forecasting.
Notice how the forecasts have captured the seasonal pattern seen in the historical data and replicated it for the next two years. These prediction intervals are a very useful way of displaying the uncertainty in forecasts.
In this case, the forecasts are expected to be very accurate, hence the prediction intervals are quite narrow. Time series forecasting uses only information on the variable to be forecast, and makes no attempt to discover the factors which affect its behavior. Therefore it will extrapolate trend and seasonal patterns, but it ignores all other information such as marketing initiatives, competitor activity, changes in economic conditions, and so on.
Time series models used for forecasting include ARIMA models, exponential smoothing and structural models. These models are discussed in Chapters 6, 7, and 8. Predictor variables and time series forecasting Predictor variables can also be used in time series forecasting.
For example, suppose we wish to forecast the hourly electricity demand ED of a hot region during the summer period. The relationship is not exact—there will always be changes in electricity demand that cannot be accounted for by the predictor variables.
Forecasting: Principles and Practice – 2nd Edition
Forecasting: principles and practice 9 Because the electricity demand data form a time series, we could also use a time series model for forecasting. Here, prediction of the future is based on past values of a variable, but not on external variables which may affect the system. Again, the "error" term on the right allows for random variation and the effects of relevant variables that are not included in the model. There is also a third type of model which combines the features of the above two models.
These types of mixed models have been given various names in different disciplines. They are known as dynamic regression models, panel data models, longitudinal models, transfer function models, and linear system models assuming f is linear. These models are discussed in Chapter 9. An explanatory model is very useful because it incorporates information about other variables, rather than only historical values of the variable to be forecast.
However, there are several reasons a forecaster might select a time series model rather than an explanatory model. First, the system may not be understood, and even if it was understood it may be extremely difficult to measure the relationships that are assumed to govern its behavior.
Second, it is necessary to know or forecast the various predictors in order to be able to forecast the variable of interest, and this may be too difficult. Third, the main concern may be only to predict what will happen, not to know why it happens.
Finally, the time series model may give more accurate forecasts than an explanatory or mixed model. The model to be used in forecasting depends on the resources and data available, the accuracy of the competing models, and how the forecasting model is to be used.
Notation For cross-sectional data, we will use the subscript i to indicate a specific observation. For example, yi will denote the ith observation in a data set.
We will also use N to denote the total number of observations in the data set. For time series data, we will use the subscript t instead of i. For example, yt will denote the observation at time t. We will use T to denote the number of observations in a time series. When we are making general comments that could be applicable to either cross-sectional or time series data, we will tend to use i and N.
Case 1 The client was a large company manufacturing disposable tableware such as napkins and paper plates. They needed forecasts of each of hundreds of items every month. The time series data showed a range of patterns, some with trends, some seasonal, and some with neither.
At the time, they were using their own software, written in-house, but it often produced forecasts that did not seem sensible. The methods that were being used were the following: 1. They required us to tell them what was going wrong and to modify the software to provide more accurate forecasts.
Case 2 In this case, the client was the Australian federal government who needed to forecast the annual budget for the Pharmaceutical Benefit Scheme PBS. The PBS provides a subsidy for many pharmaceutical products sold in Australia, and the expenditure depends on what people download during the year.
In order to forecast the total expenditure, it is necessary to forecast the sales volumes of hundreds of groups of pharmaceutical products using monthly data. Almost all of the groups have trends and seasonal patterns. The sales volumes for many groups have sudden jumps up or down due to changes in what drugs are subsidised. The expenditures for many groups also have sudden changes due to cheaper competitor drugs becoming available.
Thus we needed to find a forecasting method that allowed for trend and seasonality if they were present, and at the same time was robust to sudden changes in the underlying patterns.
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It also needed to be able to be applied automatically to a large number of time series. Case 3 A large car fleet company asked us to help them forecast vehicle re-sale values. They download new vehicles, lease them out for three years, and then sell them. Better forecasts of vehicle sales values would mean better control of profits; understanding what affects resale values may allow leasing and sales policies to be developed in order to maximize profits.
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At the time, the resale values were being forecast by a group of specialists. Unfortunately, they saw any statistical model as a threat to their jobs and were uncooperative in providing information. Nevertheless, the company provided a large amount of data on previous vehicles and their eventual resale values. The company required forecasts of passenger numbers for each major domestic route and for each class of passenger economy class, business class and first class.
The company provided weekly traffic data from the previous six years. Air passenger numbers are affected by school holidays, major sporting events, advertising campaigns, competition behaviour, etc. School holidays often do not coincide in different Australian cities, and sporting events sometimes move from one city to another.
A new cut-price airline also launched and folded. Towards the end of the historical data, the airline had trialled a redistribution of some economy class seats to business class, and some business class seats to first class. After several months, however, the seat classifications reverted to the original distribution. Step 1: Problem definition. Often this is the most difficult part of forecasting.
Defining the problem carefully requires an understanding of the way the forecasts will be used, who requires the forecasts, and how the forecasting function fits within the organization requiring the forecasts. A forecaster needs to spend time talking to everyone who will be involved in collecting data, maintaining databases, and using the forecasts for future planning. Step 2: Gathering information. There are always at least two kinds of information required: a statistical data, and b the accumulated expertise of the people who collect the data and use the forecasts.
Often, it will be difficult to obtain enough historical data to be able to fit a good statistical model. However, occasionally, very old data will be less useful due to changes in the system being forecast. Step 3: Preliminary exploratory analysis.
Always start by graphing the data. Are there consistent patterns?
Is there a significant trend? Is seasonality important? Is there evidence of the presence of business cycles? Are there any outliers in the data that need to be explained by those with expert knowledge? How strong are the relationships among the variables available for analysis? Various tools have been developed to help with this analysis.
These are discussed in Chapters 2 and 6. Step 4: Choosing and fitting models. The best model to use depends on the availability of historical data, the strength of relationships between the forecast variable and any explanatory variables, and the way the forecasts are to be used. It is common to compare two or three potential models. Each model is itself an artificial construct that is based on a set of assumptions explicit and implicit and usually involves one or more parameters which must be "fitted" using the known historical data.
We will discuss regression models Chapters 4 and 5 , exponential smoothing methods Chapter 7 , Box-Jenkins ARIMA models Chapter 8 , and a variety of other topics including dynamic regression models, neural networks, and vector autoregression in Chapter 9. Step 5: Using and evaluating a forecasting model.
Once a model has been selected and its parameters estimated, the model is used to make forecasts. The performance of the model can only be properly evaluated after the data for the forecast period have become available. A number of methods have been developed to help in assessing the accuracy of forecasts. There are also organizational issues in using and acting on the forecasts.
A brief discussion of some of these issues is in Chapter 2. So, until we know the sales for next month, it is a random quantity.
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Because next month is relatively close, we usually have a good idea what the likely sales values could be. On the other hand, if we are forecasting the sales for the same month next year, the possible values it could take are much more variable. In most forecasting situations, the variation associated with the thing we are forecasting will shrink as the event approaches. In other words, the further ahead we forecast, the more uncertain we are.
When we obtain a forecast, we are estimating the middle of the range of possible values the random variable could take. Very often, a forecast is accompanied by a prediction interval giving a range of values the random variable could take with relatively high probability.
A forecast is always based on some observations. Suppose we denote all the information we have observed as I and we want to forecast yi. We then write yi I meaning "the random variable yi given what we know in I".
The set of values that this random variable could take, along with their relative probabilities, is known as the "probability distribution" of yi I. In forecasting, we call this the "forecast distribution".
When we talk about the "forecast", we usually mean the average value of the forecast distribution, and we put a "hat" over yy to show this. With time series forecasting, it is often useful to specify exactly what information we have used in calculating the forecast. For each of the four case studies in Section 1. For cases 3 and 4 in Section Section 1.
For case 3 in Section Section 1. Principles of forecasting: a handbook for researchers and prac- titioners. Fildes Principles of business forecasting.
Added by Tim Matteson 0 Comments 1 Like. Added by Tim Matteson 0 Comments 0 Likes. Follow us: Top Content Archives. Free Online Book: I hope this help.
Thanks for the book link! This is a great book, thanks for sharing it here. Thank you. Thank you! DSC Webinar Series: Predictive Analytics: Add Videos View All. Please check your browser settings or contact your system administrator.In this book, we will not make this distinction—we will use the words interchangeably. The best model to use depends on the availability of historical data, the strength of relationships between the forecast variable and any explanatory variables, and the way the forecasts are to be used.
Thus we needed to find a forecasting method that allowed for trend and seasonality if they were present, and at the same time was robust to sudden changes in the underlying patterns. The sudden drop at the end of each year is caused by a. In most forecasting situations, the variation associated with the thing we are forecasting will shrink as the event approaches.
Please check your browser settings or contact your system administrator. Whatever the circumstances or time horizons involved, forecasting is an important aid to effective and efficient planning. It uses R, which is free, open-source, and extremely powerful software.
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