I’m excited to talk about forecasting because it’s the next important part of our project. Forecasting is the process of making future predictions utilizing information from the past and present. It’s a commonly used technique to forecast consumer behavior, market trends, and the outcomes of governmental changes in several fields, including economics.
What is Forecasting?
Statistical models are used to forecast future values of a variable, such as sales volume, stock prices, or in this case, economic indicators. It’s like looking into a crystal ball and predicting the future by looking at historical patterns.
Why is Forecasting Important?
We forecast primarily in order to plan ahead and make educated decisions. Companies use forecasting to develop strategy, budget resources, and manage inventory. Governments use forecasts to plan policies and get ready for changes in the economy. More precise forecasting can result in more effective strategic planning and decision-making.
Where is Forecasting Used?
Many different sectors and companies use forecasting. It is essential for public planning and environmental management, retail organizations use it to forecast demand to manage stock levels, the transportation sector uses it to forecast travel trends to optimize timetables and routes, and the financial industry utilizes it to predict market movements.
How is Forecasting Conducted?
Broadly defined, the process of forecasting comprises data collection and analysis, model selection, and forecast generation using the chosen model. The specific forecasting goals and the kind of data determine which model is best. The process could be as simple as extending the current trend into the future or as complex as using computers to predict shifts in the stock market.
One of the types we use for our project is SARIMA. Seasonal ARIMA is another name for SARIMA, or Seasonal AutoRegressive Integrated Moving Average. It is an improved ARIMA model designed specifically to handle data from time series containing seasonal components. Because SARIMA is effective at capturing both the regular patterns and the periodic swings in the data, it is particularly helpful for datasets where seasonality is significant, like our economic indicators collection.
Our study will forecast the economic indices of Boston using SARIMA. This model matches our dataset excellently, which has both clear seasonal tendencies and more general patterns. With SARIMA, we hope to provide precise and enlightening predictions on a number of economic factors, including real estate values and job vacancies.