The basis of my forecasting method pdf download

Several of the statistical forecasts chosen were based on default parameter values e. For a given series, the mean squared error MSE of the chosen forecasting method can be compared with that of the available method offering the lowest MSE on that series. We will define the ratio of the two MSEs as the overall fit ratio.

MSE of chosen method i. The mean fit ratio of methods selected by subjects averaged over all twenty series was 1. Nevertheless, as table 1 shows, this overall mean obscures the fact that there were considerable differences between the mean fit ratios obtained by individual subjects.

This can be seen by considering the ratio of the fit of the chosen method to the best fitting method they saw, i. Perhaps because of fatigue or increasing familiarity with the task, subjects tended to try more methods for the ten earliest series that they saw an average of 3.

Why did subjects try out more methods on some series than others? One possibility is the difficulty of modelling the patterns of particular series -the more difficult the pattern, the more methods people will try. However, the levels of sales in the different series varied considerably so that they appeared on graphs with different vertical scales. As a result a forecast error of 10 units in one series might appear to subjects to represent a larger gap than an error of units in another series.

Thus, although subjects in general did not tend to choose the best fitting methods, the perceived lack of fit of the methods they tried may have spurred them on to try further methods2. Consequences of trying more methods In general, series which had more statistical methods applied to them were forecasted less accurately the correlation between number of methods tried and the mean absolute percentage error MAPE of the chosen statistical forecast was 0.

This is perhaps not surprising as the above analysis suggests that more methods were tried on the more difficult-to- forecast series. Did people who tried more forecasting methods than their fellow subjects obtain more accurate final forecasts? In general, the answer was 'no' -the correlation between the total number of methods subjects tried and the MAPE of their chosen statistical forecasts was Did trying more methods lead to a mean overall fit ratio closer to 1.

Nor was their any evidence that subjects who tried more 1 This figure includes times when subjects revisited a method that they had already tried on a given series 2 Other variables were investigated which might have explained the variation between series in the number of methods tried.

However, it is important to note that these correlations also mask some interesting differences between subjects which will be explored later. Adjustment behaviour Why do subjects decide to apply a judgmental adjustment to particular statistical forecasts, while leaving other forecasts unadjusted? This can be investigated both in terms of the characteristics of the different series and also in relation to the behaviour of the different subjects.

Many judgmental adjustments made by subjects were relatively small -half of the absolute percentage adjustments were below 2. However, subjects who chose statistical forecasts that provided relatively poor fits to the past observations did tend to adjust their forecasts more often and make bigger adjustments. The correlation between the mean 'overall' fit ratio of the forecasting methods chosen by subjects and the number of adjustments they applied was 0.

All of this suggests that subjects who could only obtain poorly fitting statistical methods recognised the inadequacy of their forecasts and tried to compensate by applying judgmental adjustments to them.

There is some and the complexity of the series measured on a scale using a score of 1 for a 'flat' underlying pattern, 2 for a trend and 3 for an erratic underlying pattern.

All of these variables yielded correlations that were close to 0. For example, before starting the experiment, subjects were asked to indicate on a five-point scale their strength of agreement with the statement that "statistical forecasts are less important than human judgment".

The correlation between their strength of agreement with this statement and the number of judgmental adjustments they made was only 0. While this is perhaps not surprising when the adjustment were applied to well-fitting methods, the predictions also show that making large adjustments to poorly fitting methods also tended to reduce accuracy. Thus it appears that subjects who attempted to compensate for poor fitting forecasting methods by making relatively large judgmentally adjustments to their forecasts tended to be less accurate than those who obtained well fitting methods, in the first place, and made no or very minor adjustments.

Other points Interestingly, subjects who spent a larger percentage of their total time on the trial run tended to achieve more accurate forecasts. Similarly, the correlation between the actual time spend on the trial run and the MAPE was This may reflect the commitment of the individual subjects or it may indicate that time spent exploring and practising using an FSS is beneficial.

Subjects perceptions of the 'ease of use' of the FSS, 'usefulness of the FSS' and their 'assessment of their own performance' were elicited in the post-experiment questionnaire and scores constructed for each of these three dimensions. Performance was therefore not associated with the extent to which the FSS was regarded as "easy to use" and "useful".

Were the forecasters consistent? How consistent were subjects in applying particular strategies? As indicated earlier, consistency would be necessary for an FSS to recognise particular individual traits so that appropriate guidance could be provided. These correlations indicate high levels of consistency, particularly for the mean number of forecasting methods that were examined for each series and for the frequency with which judgmental adjustments were made.

The high value of the canonical correlation coefficient, which reflects the correlation across all four characteristics in table 4, is also indicative of consistency. Characterising Forecaster Behaviour: Analysis of subjects by sub-groups When designing an FSS, the system designer will typically have some stylised representation of the potential client and the tasks they expect to undertake. It is therefore valuable to classify the types of strategies that subjects used, together with the effectiveness of the different approaches.

For example, in general there may be no relationship between the number of statistical methods tried and the accuracy of the resulting forecasts. However, for some subjects trying a large number of methods may be an essential part of an effective forecasting strategy in that it used to explore and gain insights into the forecasting problem before making a commitment to a forecast.

In other cases trying a large number of methods be may symptomatic of 'thrashing around' in desperation in an attempt to find an acceptable model. The variables were standardized and, as recommended for example by Sharma, , several other clustering methods were also used and the results compared.

One subject would not fit easily into any of the clusters and was removed from the analysis; this demonstrates the difficulty of trying to find a categorization of user types that includes all possible users.

From this cluster analysis, three groups were identified and, for ease of reference, names assigned to them. These groups are described below and the data relating to them is summarised in table 6. Group 1: "The Exemplars" Fifty-five percent of subjects excluding the outlier were assigned to this group who achieved the most accurate forecasts.

Group 2: "The Sceptics" Twenty-nine percent of subjects were assigned to this group who had the least engagement with the statistical facilities of the forecasting support system suggesting a degree of sceptism. Conclusions This study has shown that there can be considerable variation in the approaches people adopt when using a forecasting support system FSS. This can occur even where these people indicate that they have similar levels of familiarity with the methods available in the system.

Most people do not tend to emulate mechanical forecasting systems by choosing the best fitting forecasting method. They also tend to examine only a small number of methods before making a selection; however, they are likely to examine more methods when they perceive the series to be difficult to model.

People who are relatively unsuccessful in identifying a well fitting statistical method tend to compensate for this by making large judgmental adjustments to the statistical forecasts. A key result of the study is that users were consistent in their approach throughout the twenty forecasts they made subject to a general tendency to try fewer methods as time went on. This suggests that adaptive FSSs could be designed to recognise particular strategies at an early stage, enabling the interface to adapt to the particular needs, strengths and weaknesses of these users.

Investors have been trying to find a way to predict stock prices and to find the right stocks and right timing to buy or sell. To achieve those objectives, and according to [2], [] some research used the techniques of fundamental analysis, where trading rules are developed based on the information associated with macroeconomics, industry, and company. The authors of [5] and [6] said that fundamental analysis assumes that the price of a stock depends on its intrinsic value and expected return on investment.

Consequently, the stock price can be predicted reasonably well. Most people believe that fundamental analysis is a good method only on a long-term basis. However, for short- and mediumterm speculations, fundamental analysis is generally not suitable. Some other research used the techniques of technical analysis [2], in which trading rules were developed based on the historical data of stock trading price and volume.

Technical analysis as illustrated in [5] and [7] refers to the various methods that aim to predict future price movements using past stock prices and volume information. It is based on the assumption that history repeats itself and that future market directions can be determined by examining historical price data. Thus, it is assumed that price trends and patterns exist that can be identified and utilized for profit.

Most of the techniques used in technical analysis are highly subjective in nature and have been shown not to be statistically valid. Recently, data mining techniques and artificial intelligence techniques like decision trees, rough set approach, and artificial neural networks have been applied to this area [8].

Data mining [9] refers to extracting or mining knowledge from large data stores or sets. Some of its functionalities are the discovery of concept or class descriptions, associations and correlations, classification, prediction, clustering, trend analysis, outlier and deviation analysis, and similarity analysis. Data classification can be done in many different methods; one of those methods is the classification by using Decision Tree. It is a graphical representation of all possible outcomes and the paths by which they may be reached.

The development of powerful communication and trading facilities has enlarged the scope of selection for investors. In order to be able to extract such relationships from the available data, data mining techniques are new techniques that can be used to extract the knowledge from this data. For that reason, several researchers have focused on technical analysis and using advanced math and science.

Extensive attention has been dedicated to the field of artificial intelligence and data mining techniques [11]. The system was trained and tested with past price data from Hong Kong and Shanghai Banking Corporation Holdings over the period from January to December Thirty Day Forecasting. Thirty Day Forecasting Book Review:. Rainwater Management Theory and Practice. Author : M. History of the Meteorological Office. History of the Meteorological Office Book Review:.

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