COMMODITY PRICE FORECASTING USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Price forecasting is the process of estimating a resource’s or commodity’s price based on a variety of factors, including the resource’s attributes, demand, projections, pricing of complementary or rival commodities, offers from different suppliers, and so on. The cost of the commodities varies constantly and is dependent on a number of variables. Must Read: Tomas Von Reckers
WHY PURCHASE PREDICTION?
User travel applications like Subway line or Hopper, which promote customer interaction and loyalty, may include price predicting features. At the same time, data from price forecasting may be useful to other businesses. To adjust the pricing of goods and activities that need a commodity (such as timber, coffee, or gold), businesses may need to decide the optimal time to buy that commodity. They may also need to consider the financial attraction of fixed assets.
In order to strategically buy and price goods that utilise these resources as raw materials, commodity price forecasting is crucial. Planners can connect platforms proactively, remove valuation risks, and make data-driven decisions thanks to forecasting, all without having to worry about disruptions to their procurement processes owing to price changes.
Regression analysis is a mathematical technique used to examine the relationship between dependent and independent variables (such as the cost of energy, a trip, or a piece of property) and one or more independent (interdependent) factors, frequently referred to as predictors, that have an impact on the target variable. To determine how much these variables have an influence on a target variable, researchers can also employ regression analysis. In regression, a parameter is always numerical.
USE OF MACHINE LEARNING IN STOCK PRICE PREDICTION:
Businesses can estimate the future value of a company’s stock and other bank deposits that are traded on a market with the help of shares pricing using deep learning. Making significant gains is the primary goal of stock price forecasting. The future performance of the stock market is impossible to forecast. Physical and psychological factors, reasonable and irrational behaviour, and other factors can also affect prediction. Together, these components contribute to the dynamic and erratic nature of stock prices. Because of this, anticipating stock prices accurately is quite difficult.
PREDICTIVE AND DEFINITIVE ANALYTICS ARE THE TWO TYPES OF PRICE FORECASTING ANALYTICS THAT CAN BE GENERALLY USED.
DESCRIPTIVE ANALYTICS: Descriptive analytics. The foundation of descriptive analytics is the statistical methods of data collection, analysis, interpretation, and presentation of findings. Raw data from observations may be converted via descriptive analytics into knowledge that is understandable and communicable. In a word, this analytics category assists in determining the “What happened?” inquiry and produces results in accordance with that answer.
Predictive analytics. Predictive analytics in the context of price projections requires looking at both recent and previous data to gauge the possibility of future occurrences, results, or values. Predictive analytics requires a variety of statistical techniques, including data mining (the discovery of patterns in data) and machine learning.
BENEFITS OF COMMODITY PRICE FORECASTING: PREDICTION OF FUTURE DEMAND
Pricing of raw materials has a significant impact on demand in a small number of industrial enterprises, which helps with demand forecasting.
PRICING OF COMPLETED GOODS:
Forecasting raw material prices is important for product pricing and market dynamics. In the semi-finished goods sector, the cost of raw materials often includes some portion of the ultimate price point.
CHALLENGES OF COMMODITY PRICE FORECASTING:
Estimating the cost of a product is a challenging undertaking since essentially similar commodities with slight variations in brand name, extra specifications, quality, demand, and other factors can have wildly different prices.
To judge how effectively or poorly our model works, we need a yardstick. In terms of machine learning, this is known as a yardstick performance measure or just a metric. The effectiveness of a regression model may be evaluated using a number of metrics, including squared error, mean absolute error, and others.
SUPPLY CHAIN DIFFICULTIES:
The coronavirus epidemic’s impact on the supply chain has resulted in extreme price volatility in recent months, putting the discipline of commodities price forecasting to the absolute test. To provide real-time pricing monitoring and tracking across areas based on macrotrends like worldwide pandemic developments, border closures, labour market halts, and new laws, regulations, and policies, our team continues to work with our customers.
MARKET TREND AND PRICE FLUCTUATIONS:
Most local and international stainless-steel businesses will have reduced profit margins. According to SpendEdge’s most current oil price forecast for 2019, a strong increase in crude oil market prices will increase utility costs, particularly those for gasoline and lubricants. Costs associated with making and delivering stainless steel products will rise as utility prices rise. They will have to increase the price of finished items to pay their production expenses as a result of the rise in the price of crude oil on the market.
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NECESSITY OF FORECASTING BASED ON ARTIFICIAL INTELLIGENCE:
Both quantitative and qualitative evaluations of the supply and demand sector indicators or statistical models using univariate methodology that exclusively rely on historical pricing data are the two main types of traditional forecasting techniques. These methods, however, fall short of capturing all market variables and do not perform well for predicting over longer time periods (weekly/monthly). On the other side, forecasting models powered by AI/ML may produce more precise forecasts over a wider time span. In order to help organisations make better decisions, these algorithms can also examine vast volumes of historical data and reveal hidden trends.
PRICE FORECASTING USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING:
Commodity price forecasting is a crucial process for a variety of enterprises. Decisions about price forecasting directly affect a company’s bottom line and have an impact on everything from relationships to strategic business strategy. Because of this, it is an area that requires incredibly precise knowledge and insights.
When it comes to necessities like crude oil or gold, there are several levers and metrics to look at. On paper, this qualifies them as a superb use case for limited AI – an analysis task involving a sizable amount of manual labour and the processing of enormous volumes of data.