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Commodity forecasting  has long been a crucial undertaking for organizations whose operations depend on raw resources. How precise these forecasts are can have a significant impact on an organization's revenue, perceived risk, and capacity of making informed decisions. The advancement of artificial intelligence (AI) and machine learning technologies has improved commodity forecasting, enabling businesses to lower risks and make more informed decisions.

 

Businesses can reduce risk and make better decisions when forecasting commodities with the aid of artificial intelligence (AI) machine learning (ML). Large volumes of data are collected, analyzed, and acted upon by AI and ML systems, which subsequently take intelligent action. AI has been able to spot trends in price variations by examining past data on commodity pricing and using statistical models. This has helped businesses decide when and where to place orders for commodities from suppliers. Businesses may improve operations and better fulfill customer demand with the help of this data-driven forecasting.

AI/ML used for commodity forecasting

 

Commodity forecasting uses AI/ML to analyze past and real-time data to recognize trends and patterns that can be used to predict future  commodity prices . Some AI/ML applications for commodities forecasting include the ones listed below:

 

1.  Data gathering and processing

 

AI/ML algorithms can collect and interpret a lot of data from various sources, such as financial news, weather reports, and social media, to offer a holistic picture of the market.

 

2.  Modeling that Predicts

 

Using current and historical data, machine learning algorithms assist in the construction of predictive models that calculate commodity prices in the future. Developments in the marketplace or other possible effects on commodity pricing can be accounted for by altering these algorithms.

 

3.Risk assessment

 

AI/ML can be used to analyze the risk associated with buying a certain commodity. This methodology can be used to determine the probability of a decline in commodity prices and the necessary mitigating actions.

 

4. Sentiment analysis

 

AI/ML might look at social media posts and news stories to determine how the public generally feels about a certain product or service. Traders can utilize this study to comprehend the viewpoints of market participants on the commodities and potential future price adjustments.

 

5. Optimization

 

By using forecasting analytics, AI/ML can decide when it is optimal to buy and sell commodities, reducing the likelihood that a mistake will be made.

 

 

Advantages of commodities forecasting based on AI/ML: 1. Risk Mitigation

 

One of the key advantages of AI/ML-based commodities forecasting is its capacity for risk mitigation. Many variables, including supply and demand, the environment, geopolitical events, and others, affect the price of commodities. Businesses can reduce the risk of losses by using accurate forecasting to better understand these issues and modify their tactics. Moreover, organizations can get significant insight into market situations with AI/ML-based forecasting, allowing them to react to changing conditions swiftly.

 

 

2. Enhancing Decision-Making

 

Enhancing Decision-Making:  Businesses may make better decisions about whether to buy or sell commodities by giving them precise and up-to-the-minute market knowledge. This can aid companies in maximizing gains and reducing losses. Moreover, AI/ML-based forecasting can give companies an edge over rivals by enabling them to take quicker and more precise decisions.

 

3. Cost Savings 

 

Commodity forecasting powered by AI/ML can potentially result in cost reductions. By enabling them to more accurately predict demand and change their output in response, accurate forecasting may assist firms to streamline their processes and cut waste. Also, organizations can save resources that would have been spent on policy or other ways to mitigate risk by lowering the likelihood of losses.

 

 

Best practices for commodity forecasting with AI/ML 1. Selecting the appropriate data and models

 

Choosing the appropriate data and models is one of the most crucial aspects of AI/ML-based commodities forecasting. The accuracy, relevance, and timeliness of the data utilized during the forecast model must be guaranteed. Furthermore, the models must be suitable for the particular commodity being projected and be able to take into consideration any pertinent market conditions. It could be important to employ many models, keep track of their progress, and modify them as needed as market circumstances shift to assure accuracy and dependability.

 

2. Working together with Subject Matter Experts

 

These can include statisticians, economists, and other specialists with an in-depth understanding of the commodities market. Businesses may recognize possible threats and opportunities, obtain a full grasp of economic conditions, and create more precise forecasting models by collaborating with professionals.

 

3. Providing accountability and transparency

 

Moreover, it is crucial to guarantee accountability and openness in the estimation methods. As well as making sure stakeholders are aware of the predicting approach and data sources, this calls for making the prediction accountable and subject to criticism. Also, companies should constantly check and assess the reliability and accuracy of their forecasting systems.

 

 

 

Conclusion:

 

Commodity forecasting powered by AI and ML has emerged as a key tool for organizations to reduce risk and enhance decision-making. Going ahead, the potential of AI/ML-based commodities forecasting is hopeful as businesses grow more skilled at leveraging it to make educated decisions and technology continues to advance. Pricevision  is here to get you started with the powerful AI/ML based commodity forecasting.

The commodity market, which is a crucial component of the world economy, is always changing. A difficult challenge, given the complexity of the market, has always been predicting the prices of commodities. Yet, commodity price forecasting has become more precise and effective than ever before thanks to technological breakthroughs, particularly those in artificial intelligence (AI) & machine learning (ML). The commodities market, which trades in a variety of things including metals, minerals, agricultural products, biofuels, and other goods, is an essential part of the global economy. Commodity markets, such as wheat as well as oil, have a big impact on businesses and economies all over the globe. Making good investing selections implies having a strong grasp of the commodities market's characteristics. The world's economy is seriously affected by the competitive and intricate commodity market. It might be challenging to forecast what certain commodities will cost in the future because their prices fluctuate often.

 

Forecasting for Big Commodities 

 A crucial component of the commodity market is the forecasting of major commodities. Forecasts are used by investors and traders to make well-informed decisions on the purchase and sale of commodities. Commodity price predictions are made using a variety of techniques, such as value investing, technical analysis, or machine learning algorithms.

1. To ascertain a commodity's true value, fundamental analysis, a method of forecasting, examines economic and market data. This approach takes into account variables that can affect a commodity's price, including demand and supply, cost of production, and geopolitical concerns.

2. Technical analysis is a method for predicting future price movements by examining past price swings and market trends. This approach makes use of graphs, charts, and chart patterns to spot market patterns and trends.

3. Machine learning algorithms sift through enormous volumes of data to find underlying trends and patterns which can be employed to anticipate future price changes with accuracy.

 

The Function of AI ML in Predicting Commodities Prices 

The commodity market is transforming thanks to AI and ML, which are giving traders and investors strong tools for price forecasting. Many types of data, such as market trends, historical pricing, production levels, weather patterns, and geopolitical events, can be analyzed by AI and ML algorithms. AI and ML systems can generate precise forecasts about the future pricing of commodities by spotting indications of trends in this data.

 

The ability of AI and ML to evolve and change over time is one of the biggest benefits of commodity price predictions. The accuracy of AI and ML systems can be improved as more information is made accessible. This implies that based on the most recent and precise price predictions, traders & investors can conduct more educated judgments.

 

Real-time commodities futures prices and the commodity market

 

Commodities including metals, energy, food, and cattle are traded on the global commodity market, which is open around the clock. Demand and supply, as well as other elements like production levels, climatic conditions, and geopolitical events, all affect how much certain commodities cost. Traders and investors utilize real-time commodities futures prices to make predictions about these commodities' future prices. Futures prices indicate anticipated prices at a future time and are based on the costs of the underlying commodities.

 

Trends in the Commodity Market

 

Trends in the commodity market have a significant impact on commodity prices. These patterns are based on several variables, including shifts in supply and demand, developments in technology, and governmental regulations. For investors and traders, comprehending these trends is essential since it enables them to choose wisely when to buy and sell commodities.

For instance, the price of oil and gas has significantly changed as a result of the shift toward renewable energy sources. The need for natural gas and petroleum is anticipated to fall as more nations transition to renewable energy, which will have an impact on these commodities prices. The price of grain as well as other agricultural commodities has also been impacted by the trend toward sustainable and organic farming.

 

The Use of AI/ ML to Predict Commodity Prices in the Future

 

The use of AI/ ML to forecast commodities prices has a bright future. AI/ ML algorithms will grow progressively more sophisticated and potent as technology advances. This will make it possible for investors and traders to estimate commodity prices more precisely, which will result in more lucrative investments.

 

The prediction for short-term price changes is one area where AI/ ML is already making substantial progress. These algorithms are capable of analyzing real-time data and giving traders the most recent price forecasts. This enables traders to make prompt judgments and profit from swift price changes.

 

The forecasting of long-term trends is yet another area where AI/ ML is anticipated to have a substantial impact. AI/ ML algorithms can give traders and investors insights into long-term price fluctuations by examining previous data and market trends. This will facilitate them to make better choices about long-term investments.

 

Conclusion

 

Commodity price forecasting is evolving thanks to AI/ ML, and the future appears hopeful. Trading and investment professionals now have sophisticated tools to forecast future prices thanks to AI and ML algorithms' capacity to evaluate enormous volumes of data or learn over time. As technology advances, we can expect to witness further advanced and powerful AI/ ML algorithms that will revolutionize the commodity market. With the correct expertise and tools, dealers and investors may navigate this intriguing area and make winning bets. To learn more about commodity price forecasting, visit https://pricevision.ai/.

 

 

 

The business strategy of purchasing teams and organizations must closely monitor real time commodity price forecasts and trends. It enables companies to foresee pricing-related risks, and plan, and manage suppliers proactively while avoiding supply chain interruption brought on by price volatility.

 

Category managers have a crucial role in budget planning, and the structure of the organization, the financial and procurement teams may also play a part. Based on industry and price projection insights for the upcoming six, twelve, and eighteen months, forecasting solutions enable buyers of commodities, natural resources, and services to make confident planning decisions.

 

The ability to prioritize categories and concentrate on the most volatile ones is a key advantage of price tracking and forecasting in various categories within a manager's product portfolio. This includes looking at price hedges as a risk mitigation approach.

 

AI-Based Forecasting - Live Commodity Prices

 

Forecasting models based on AI/ML can offer more precise projections over a wider range of regions and exchanges for longer periods. Additionally, these algorithms are capable of sifting through vast volumes of previous data to uncover hidden patterns that aid businesses in making wiser and more profitable business decisions. The following are some advantages of AI-based commodities forecasting:

1. The capacity to handle extreme price fluctuation.

2. The capacity to combine many predictors from different sources.

3. Accurate forecasting across several time horizons

4. Model errors in reading, or the capacity to recognize a variable's importance.

 

Factors influencing the product Price Sensitivity

 

Supply and demand are important factors. Prices for oil, natural gas, and electricity are frequently more erratic than those for other commodities. The fact that several users have few options for switching to alternate fuels when the cost of natural gas, for instance, changes is one reason for the unpredictability of energy costs. Residential customers often cannot quickly alter their heating systems, and it might not be financially advantageous in the long run.

 

Customers can quickly replace food items when the relative costs of foodstuffs change, but they are limited in their ability to accomplish this when it comes to heating their homes.

 

How does Commodity Price Forecasting work?

 

PriceVision examines previous price changes and levels to make price predictions using specific techniques. Although this has been compared to driving a car while only looking in the rearview mirror, the wide acceptance of traders gives it credibility. By tracking prices (often futures) and searching for recurring patterns, traders forecast when price movements will alter and how both high and low prices will move.

 

Commodity markets are examined using both technical and fundamental research. Foundations, or supply/demand factors, typically provide the market with its underpinnings. Price vision is used to predict the timing and size of price changes as well as to show the general price trend. Fundamentals are the more powerful force of the two. While a market's reaction to technical indicators can have a significant impact because so many financial markets pay attention to them.

 

Charting software

 

To investigate lines and patterns, a variety of techniques are employed. They can change depending on the person, the type of evaluation, and the current style. Because so many individuals utilize those popular strategies to invest money, the most popular techniques frequently turn into the ones that are most correct. So, they might end up being a self-fulfilling prophecy. The most popular methods are:

1. High/low/close or bar charts

2. Dynamic averages

3. Pattern lines

4. Channels

5. Cycles

6. Axes of resistance and support

7. Corrections

8. Overlapping tops and bottoms

9. Forming a head and shoulders

10. Trading activity

11. Open curiosity

How PriceVision Helps in Demand Prediction

 

In order to help shops be more competitive, PriceVision helps forecast the future need for every item and obtains real-time information. Producing daily, weekly, or monthly pricing estimates, enables wise and lucrative business decisions.

1. Charts & technical analysis can be used to support your analysis.

2. Avoid losses and lost opportunities to save money.

3. predicting pricing across all regions

4. Using simple data drivers

5. ML-based prediction of commodities prices

6. more extensive time frames for commodity forecasting

 

Potential Use of Artificial Intelligence in Commodities Management

 

One thing is certain: AI would play a key role in commodities intelligence in the future. With increased volatility and unpredictability affecting commodities and more information sources are accessible to support decisions. Numerous businesses are already providing AI solutions for commodity management as a result. Since we belong to them, we should know.

 

We do, however, also understand that chasing the latest tech may not be the best course of action — acceptability, flexibility, and ROI all have an impact – and as a result, traditional approaches to commodities forecast management continue to provide a sizable amount of value.

 

Commodity managers can gain the knowledge they need from AI to improve their decisions and estimate the prices of future commodities.

 

Conclusion

 

Due to supply chain disruptions brought on by the coronavirus epidemic, prices have recently experienced unprecedented volatility, testing the basic foundations of the discipline for commodity price forecasting. As a result, the PriceVision team keeps collaborating with our clients to deliver real-time price tracking and monitoring across regions based on macro trends like global pandemic developments, border closures, labor market slowdowns, and legislative changes, rules, and regulations.

To discuss your commodity price forecasting and tracking needs or to gain a better understanding of the procurement services offered by PriceVision visit us at https://pricevision.ai/

The market provides a wide range of assets where people can invest their unused funds to make money. Investors seeking strong returns typically invest in either equities or commodities, which are two different asset classes. Stocks signify ownership in a corporation, whereas commodities are items like metals, energy, and agricultural products. Both of these asset groups have substantial potential for profit. They are exchanged, nonetheless, on various markets. Therefore, before investing in either, it is crucial to understand the differences between the stock market and the commodity market.

 

By inexperienced investors, the phrases stock market & commodity market are frequently used interchangeably. Even so, there are several key distinctions between the two that might guide your choice of investment. The distinctions between these two markets, if you're novice to investing, will become clearer as your wealth increases. Nevertheless, even seasoned investors occasionally succumb to the parallels between equities and commodities. There are certain distinctions between them, though, and we'll discuss those in this post. If you're not familiar with how the stock market operates, you might want to review the fundamentals before going any further.

 

Stock Exchange

 

It alludes to a group of stock exchanges where shares are bought, sold, and traded. As was already established, stocks represent a company's ownership. These are best understood as components of the total equity of a corporation. Each business understands only Rs. 1000 of a company's total equity if its capital is worth Rs. 1000 crores and there are 1 crore shares. One share of stock entitles the holder to only that fraction of the company's ownership.

 

The value of one's holding regularly varies with adjustments in the statement of financial position, driven about by a multiplicity of circumstances, both internally and externally. Depending on their investing goals, a person may decide to sell their stocks the same day they are purchased, a year later, or even 10 years later.

 

The stock market, which has numerous exchanges within it, is the market that makes it possible to purchase and sell. In the Indian stock market, there really are two primary stock exchanges -

 

●            National Stock Exchange

●            Bombay Stock Exchange

 

Individuals must have a trade and DEMAT account in order to invest in equities listed on either of these markets or others.

 

 

Commodity Market

 

It is a commodity market, as the name would imply. These products fall into two categories:

●            Hard commodities

●            Soft commodities

 

The former speaks of products that are mined and extracted, such as crude gold and oil. These are 2 of the most valuable and traded commodities on the planet. Rice, wheat, eggs, pigs, cattle, and other agricultural commodity and livestock items are included in the latter group. Comparatively speaking to hard goods, these often have a significantly shorter lifespan.

These products can be bought, sold, and traded in commodity markets. The trading process is one of the comparisons between commodities and stocks. The majority of dealers that trade commodities do so using futures contracts. These agreements bind the parties to carry out a transaction at the agreed-upon price and on the agreed-upon date. Futures contracts are frequently used by manufacturers and farmers as a hedge against possible losses. These, nevertheless, also serve as a remarkable tool for realising a profit.

 

A person may decide to invest immediately in commodities. To that goal, India has six commodity exchanges:

 

●            Multi Commodity Exchange (MCX)

●            Ace Derivatives Exchange (ACE)

●            The Universal Commodity Exchange (UCX)

●            National Multi Commodity Exchange (NMCE)

●            Indian Commodity Exchange (ICEX)

●            National Commodity and Derivatives Exchange (NCDEX)

 

What distinguishes the commodity market from the stock market?

 

Analyzing the influence of various economic elements on each market is crucial if one wants to clearly comprehend the differences between both the stock market or commodity market.

 

●            Inflation

A rising tendency in the prices of almost all items in an economy is referred to as inflation. Inflation typically happens along with rising consumer income. The former does, however, occasionally surpass the latter.

A commodity market flourishes in an inflationary environment because as raw material costs rise, a growing number of investors turn to those markets. As a result, the cost of manufactured items rises, which lowers consumption. It spirals into subpar performance across numerous industries, causing the stock market to move downward. It's one of the key distinctions between the stock market and the commodity market.

 

●            US dollar's value

 

The impact of USD on gold is extremely pronounced. The value of gold is inversely correlated to the US dollar. Typically, when the USD is performing poorly, investors look to gold as a safe haven. On the other hand, if the US currency strengthens, investors are less likely to like it.

 

In other instances, as in the most recent recession that shook the market in late February, this propensity for gold also correlates with such a disinterest in the stock market. Before choosing to invest in either, it is essential to understand the differences between the stock market and the commodity market. In order to make an informed choice in these marketplaces, it's crucial to analyze the possibilities available.