The Latin Language Prediction English Language Essay

Published: Last Edited:

This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.

"My salesman looks out of the window and gives me the sales forecast for the next year"said one senior manager.This salesman may be quite effective in his job,but he is only predicting and not forecasting .Forecasting is a little more scientific than looking into a crystal ball.The scientific basis of forecasting lies in studying past,present and future trends, present and future actions and their effects.What happened in the past is relevant to what is happening now and what could happen in the future.

On the basis of above informations explain forecasting taking into account three dimensions of time-----

A} Past

B} Present

C} Future

Ans - Prediction

The roots of the word "prediction" lie in the Latin language. "Prediction" is a combination of two Latin words - "Prae" meaning "Before" and "Dicere" meaning "To say". Thus, in literal terms, the word "prediction" means "to say before" or to comment on something before it happens or actually occurs. Prediction is a statement made by us about the way we expect things to happen in the future period. It is an estimate made by us on what future circumstances will be like. Predictions are often based on knowledge and experience but this is not always the case.

The future is at all times uncertain. But, the uncertainty of the future does not stop us from making plans for the same. Instead, it gives us the motivation to be prepared for all possible future outcomes so that we can make the best of what we get. And herein, lies the need and importance of predictions. For it is with the help of predictions only that we are able to find out the possibilities or the different outcomes that can be expected to occur in the future.

Predictions are oftentimes confused with guesses or informed opinions. Sometimes predictors or the people making predictions derive some particular proposition from some particular examples available or witnessed by them in the past. For example, if there are no clouds in the sky at dawn, a person might say that there will be a clear sky today. This prediction might turn out to be true. The predictor might have made this prediction based on the past sightings or his past experience. And then again, this prediction might turn out to be false if the sky becomes overcast in the afternoon. Thus, predictions are mere statements and not guaranteed outcomes. They merely inform us about the greater probability of the occurrence of one outcome over the occurrence of the other possible outcomes.

"Prediction" is often thought to be synonomous with "forecasting". However, this is not the case. Although, there is a lot in common between "prediction" and "forecasting" they are not entirely alike. There are some basic differences between the two. Thses will be delienated below as we discuss forecasting in greater detail.


Forecasting may be defined as the process of making some specific satatements in relation to some events or circumstances, the results of which, have as yet not been observed. For example, before a cricket match, a weather forecast is made, the pitch is tested and accordingly some prediction is made about the runs that each team will be able to make. Forecasting is making a specific estimate while prediction refers to making a more general estimate. Prediction could mean making an estimate about the number of earthquakes that will come in a particular region in a particular year. Forecasting refers to making an estimate of when an earthquake might occur and of what magnitude it would be.

However, there is something common to both prediction and forecasting, that is, risk and uncertainty. The future being always uncertain, there is a certain degree of risk attached whenever we make a prediction or a forecast. This degree has to be given to the receiver of the estimate so that it might be taken into account while taking any decision. The degree of risk attached to a prediction is greater that that attached to a forecast. This is why forecats are preferrede to prediction and are also considered more reliable and accurate.

Forecasting is usuallt considered a result of the union of the past, present and future. It is widely believed that what has happened will come to pass again. Alternatively, the past always affects our present and our present actions, in turn, make possible our future. Any difference in the actions of any of the two preceeding timelines (past and present) will be reflected by a consequent difference or a change in the future. Forecasting can be done by a variety of techniques. These have been discussed later in greater detail.

S. No.




Involves judgment after taking all available information into account.

Involves projection of past into future.


Involves anticipated change.

Involves estimated change.


Is more intuitive.

Is more scientific.


Is governed by personal bias and preferences.

Is free from personal bias and preferences.


Is more subjective.

Is more objective


Is popularly called "Saving Beforehand" technique..

Is popularly called "Throw Ahead" technique.


Does not contain error analysis.

Error analysis is possible.


Is reproductible i.e. same result is obtain regardless of number of times it is tested.

Is non - reproductible.

Techniques of Forecasting -

The techniques of forecastinbg can be broadly divided into two categories :-

1) Qualitative

2) Quantitative

Qualitative forecasting techniques are usually used in those situation where no data is available about the past. These techniques are based on the individual opinions, judgements, feelings, etc. of the consumers, respondents, experts, etc. Thus, these techniques are subjective in nature and are prone to being affected by bias or any partiality in judgement. These techniques are usually applied to intermediate to long - range decisions. Some examples inculde Delphi technique, Nominal Group technique, etc.

On the other hand, quantitative techniques are only applied in situations where some past data is available. This is because in these techniques it is assumed that the past is a function of the future and will be affected by the same. Thus, these techniques are not based on personal opinions but instead on empirical findings. These techniques are usually applied to short to intermediate range decisions. Some examples include trend analysis, exponential smoothing, etc.

Qualitative Techniques -

1) Informed Opinion and Judgement - This is basically a form of prediction only. In this, the opinion of an expert or someone who is knowledgeable in the field of study about which a forecast has to be made. For example, asking some cricket veterans like Kapil Dev, etc their opinions on how many runs the two teams will score based on their opinions about the pitch weather, the past performance of the two teams, etc.

2) Market Research - In this, a research is conducted by a research company, salesmen of a marketing company, etc. In this, the objective of the research is usually to establish direct contact with the customer and find out their opinions on the company's products. Opinions include what they like and dislike about the product, what improvements can be made in the product to make it more attractive to customers, etc. Such researches are generally conducted by large companies on a periodic basis so that they are always in a position to feel the pulse of the market, identify their weaknesses and competitors, etc.

3) Delphi Technique - In this technique, a panel of experts from various fields or CEOs or high ranking personnel of various firms are invited as subjects of the research. There is one panel coordinator. He puts a set of questions to the experts and asks them to write their answers down. The coordinbator then summarises the different answers, interprets them and gives another set of questions to the panel experts to answer. This process continues until the coordinator gets some common idea from all the experts and is satisfied with their answers. This technique has a very big advantage in the fact that because no discussion is allowed, there is no chance of one person dominating the discussion and / or influencing the views and opinions of others in any way. However, on the minus side, the coordinator has to have sufficient knowledge in the fields of all the experts to be able to interpret their views and question them further.

4) Nominal Group Technique - This technique is similar to Delphi Technique in that a panel of experts is selected whose opinions are taken as a part of the research. However, the similarity ends here. In this technique, the experts are seated around a table such that they are in full view of each other. A question is written on a board for all the experts and they are asked to write down their ideas about the same. After 15 - 20 minutes, the coordinator starts from the first expert and asks him to tell his ideas. These are all written on the board before the coordinator moves on to the second researcher and repeats the process with him. Thus, the coordinator gets all the ideas from all the experts by proceeding in a round robin manner. The common or similar ideas are combined into one idea and all the other ideas are discussed. The pros and cons of each idea are discussed. Then the researchers are asked to rank the ideas as 1st, 2nd, 3rd, etc. The basic advantage of this method is that it allows discussion between the experts. Thus, each idea is examined from different angles and viewpoints. Also, this process sometimes leads to the creation of a "hybrid" idea which is sometimes much better than the individual idea.

Quantitative Techniques -

1) Trend Analysis - This method involves the use of statistics to predict the future possible events or outcomes. In this method, we make use of ratios, graphs, charts, etc. to depict the information available to us aboput the past in a form such that we can compare the same. For example, by seeing the Profitability Ratio of the company for the past 5 years, we can make a forecast about the expected Profitability Ratio of the current year. This method is very useful in the preparation of budgets which involves the use of past data so as to make accorate and reasonable projections about the future.

2) Exponential Smoothing - This technique is usually applied to data that is is time series format. It is used to make forecats or to produce smoothed data for presentation. The time series data is actually a sequence of observations written down at different time intervals. This process can be both orderly and random. In this method weights are assigned to occurrences. In the process of exponential smoothing, exponentially decreasing weights are assigned over time. This technique is most suitable for use with a discrete set of repeated measurements. However, it is most commonly applied to economic data and to make forecasts about the financial markets.

When the sequence of observations begins at time t = 0, the simplest form of exponential smoothing is given by the formulae[1]:

\begin{align} s_0& = x_0\\ s_{t}& = \alpha x_{t} + (1-\alpha)s_{t-1},\ t>0 \end{align}

where α is the smoothing factor, and 0 < α < 1.

3) Naive Approach - This approach provides objective forecasts. These forecasts are also very cost - effective. This approach provides forecats which serve as a benchmark against the backdrop of which more sophisticated models can be compared with each other. This approach usually makes use of stable time series data. In its use of this data, this approach assumes that the actual value of the previous period is equal to the value of this period.

4) Causal / econometric forecasting methods - These methods operate on the assumption that it is possible to identify the underlying factors that might affect / influence the variable in relation to which the forecast is being made. For example, including information about weather conditions could help in improving the ability of a model to predict umbrella sales. This is an example of a model of seasonality, that is, a model that shows a regular pattern of up and down fluctuations. Seasonality may also be due to holidays, customs, etc.

Causal forecasting techniques are subject to the discretion of the forecaster. There are several informal methods which do not have strict algorithms, but rather modest and unstructured guidance. One can forecast based on, for example, linear relationships. If one variable is linearly related to the other for a long enough period of time, it may be beneficial to predict such a relationship in the future. The most important factor when performing this operation is using concrete and substantiated data. Forecasting off of another forecast produces inconclusive and possibly erroneous results.

Causal methods include :

1) Regression Analysis - This includes a group of methods which can be used to estimate or predict future values of a variable by making use of information about other variables and the relationship between them. These methods include parametric (non - linear / linear) and non-parametric techniques.

2) Autoregressive moving average with exogenous inputs (ARMAX)

Thus, all in all, we can say that prdictin and forecasting despite being very similar to each other and having a lot of overlap are not interchangeable terms and have some distinguishing features. ALso, forecasting, being the more scientific, is also the more reliable and accurate of the two having a lower degree of risk attached to it.