There are many methods of forecasting such as qualitative/quantitative, naïve/expert. However, these are NOT the methods normally used in the currency market. Describe the methods of forecasting in 2-3 sentence each.
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Answer:
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Explanation:
Qualitative vs. quantitative methods Edit
Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers and experts; they are appropriate when past data are not available. They are usually applied to intermediate- or long-range decisions. Examples of qualitative forecasting methods are[citation needed] informed opinion and judgment, the Delphi method, market research, and historical life-cycle analogy.
Quantitative forecasting models are used to forecast future data as a function of past data. They are appropriate to use when past numerical data is available and when it is reasonable to assume that some of the patterns in the data are expected to continue into the future. These methods are usually applied to short- or intermediate-range decisions. Examples of quantitative forecasting methods are[citation needed] last period demand, simple and weighted N-Period moving averages, simple exponential smoothing, poisson process model based forecasting [2] and multiplicative seasonal indexes. Previous research shows that different methods may lead to different level of forecasting accuracy. For example, GMDH neural network was found to have better forecasting performance than the classical forecasting algorithms such as Single Exponential Smooth, Double Exponential Smooth, ARIMA and back-propagation neural network.[3]
Average approach Edit
In this approach, the predictions of all future values are equal to the mean of the past data. This approach can be used with any sort of data where past data is available. In time series notation:
{\displaystyle {\hat {y}}_{T+h|T}={\bar {y}}=(y_{1}+...+y_{T})/T}{\hat {y}}_{{T+h|T}}={\bar {y}}=(y_{1}+...+y_{T})/T [4]
where {\displaystyle y_{1},...,y_{T}}y_{1},...,y_{T} is the past data.
Although the time series notation has been used here, the average approach can also be used for cross-sectional data (when we are predicting unobserved values; values that are not included in the data set). Then, the prediction for unobserved values is the average of the observed values.
Naïve approach Edit
Naïve forecasts are the most cost-effective forecasting model, and provide a benchmark against which more sophisticated models can be compared. This forecasting method is only suitable for time series data.[4] Using the naïve approach, forecasts are produced that are equal to the last observed value. This method works quite well for economic and financial time series, which often have patterns that are difficult to reliably and accurately predict.[4] If the time series is believed to have seasonality, the seasonal naïve approach may be more appropriate where the forecasts are equal to the value from last season. In time series notation:
{\displaystyle {\hat {y}}_{T+h|T}=y_{T}}{\hat {y}}_{{T+h|T}}=y_{T}
Drift method Edit
A variation on the naïve method is to allow the forecasts to increase or decrease over time, where the amount of change over time (called the drift) is set to be the average change seen in the historical data. So the forecast for time {\displaystyle T+h}T+h is given by
{\displaystyle {\hat {y}}_{T+h|T}=y_{T}+{\frac {h}{T-1}}\sum _{t=2}^{T}(y_{t}-y_{t-1})=y_{T}+h\left({\frac {y_{T}-y_{1}}{T-1}}\right).}\hat{y}_{T+h|T} = y_T + \frac{h}{T-1}\sum_{t=2}^T (y_{t}-y_{t-1}) = y_{T}+h\left(\frac{y_{T}-y_{1}}{T-1}\right). [4]
This is equivalent to drawing a line between the first and last observation, and extrapolating it into the future.
Seasonal naïve approach Edit
The seasonal naïve method accounts for seasonality by setting each prediction to be equal to the last observed value of the same season. For example, the prediction value for all subsequent months of April will be equal to the previous value observed for April. The forecast for time {\displaystyle T+h}T+h is[4]
{\displaystyle {\hat {y}}_{T+h|T}=y_{T+h-km}}{\hat {y}}_{{T+h|T}}=y_{{T+h-km}}
where {\displaystyle m}m=seasonal period and {\displaystyle k}k is the smallest integer greater than {\displaystyle (h-1)/m}(h-1)/m.
The seasonal naïve method is particularly useful for data that has a very high level of seasonality.
Time series methods Edit
Time series methods use historical data as the basis of estimating future outcomes. They are based on the assumption that past demand history is a good indicator of future demand.
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