In this video, we will explore what forecasting is and how to understand some of the terms used in forecasting. Finally, we will look at how to select the right model for a given dataset and the type of problem we are asked to solve. Everyday at all levels of the financial industry, decisions are made about what is likely to happen in the future. Businesses take action today based on yesterday's data and tomorrow's expectations. You can call them expectations, predictions, or projections. It all means one thing: forecasting. We will try to understand terminology and how to choose the right model for our forecasting needs. Finally, we will look at how we can use BigQuery's latest edition machine learning to make forecast. Forecasting is a technique used by businesses for predicting the future based on past information. The forecast can be in terms of either dollars such as revenue or some physical volume such as stock returns or absolute prices. Predicting, projecting, or estimating some future volume or financial situation is very critical, and traders use it all the time without realizing that they are performing a forecast. A trader does not buy a stock, for example, without having some expectation of where it'll be tomorrow, or next week, or next year. Wikipedia says that forecasting is the process of making predictions of the future based on past and present data and most commonly by analyzing trends. There are two basic ways to make a forecast: one is a qualitative and the other is quantitative. A qualitative forecast would involve at least two usually multiple rounds of experts answering questions and giving justifications for their answers, providing the opportunity within grounds for changes and/or revisions. 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're usually applied to intermediate or long-range decisions. Examples of qualitative forecasting methods are informed opinion and judgment, market research, and historical life cycle analogy. On the other hand, 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. On the other hand, a quantitative forecast models the relationship between two or more explanatory variable and a response variable. This can be of two kinds: causal and time series. In causal models such as regression, we model with a single explanatory variable or a more complex model with multiple variables. You can think of it as fitting a linear equation to observe data or an exponential function in multidimensional space to multiple data points. Time series methods, however, use a function of last period's actual value as a forecast for the future. You can think of this as technical trading to use trading terms. That is, you're using past stock prices to predict future stock prices. In one case, we use a simple moving average model which uses an average of a specified number of the most recent observations with each observation receiving the same emphasis or weight. A weighted moving average model on the other hand uses an average of a specified number of the most recent observations with each observation receiving a different emphasis or weight. There are several variations to using past data. But what we must understand is that in time series models, we use past prices to forecast future prices or past volume to forecast future volume. Nothing else. To summarize, in both regression and time series forecast, we need to collect data, organize it, create a model, experiment a lot with it like tweaking it, and finally produce results that we think are acceptable. However, there is a crucial difference. In regression, we use explanatory variables to explain the response which means to predict that variable. In time series forecast, however, or simply called forecasting, we use past data to predict future data. There is no other variable, explanatory or otherwise. Hope this distinction is clear. Now, let's look at the way in which we will use both methods to predict stock prices in the future.
