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Price Forecasting with Artificial Intelligence & Machine Learning technology

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The crystal balls have been replaced by algorithms, making price forecasting an efficient asset for business strategy and decision-making. Predicting future prices of goods and services enables companies to control risks and maintain a competitive advantage.

Today, AI algorithms can process vast data sets to spot patterns and predict business outcomes.

These algorithms also consider external influences like economic indicators, weather conditions, and geopolitical developments, improving the precision of their predictions. Let’s find out what benefits price forecasting can yield for your business.

Understanding price forecasting

Price forecasting uses historical data, statistical methods, and various analytical tools to estimate future prices of specific products, services, or assets.

The goal is to accurately predict future prices, enabling businesses to make strategic decisions regarding production or marketing.

Effective forecasting helps companies anticipate shifts in demand, supply, and overall market dynamics, facilitating risk management, inventory optimisation, and resource management.

Descriptive and predictive analytics

Descriptive analytics employs statistical methods for data collection, analysis, interpretation, and presentation. This approach transforms raw data into understandable knowledge, answering the question, “What happened?”.

On the other hand, predictive analytics focuses on using current and historical data to envision future events, outcomes, or values, particularly in price forecasting.

It involves various statistical techniques to identify data patterns, including data mining and machine learning.

Electricity price forecasting, real estate price forecasting, fuel prices, energy price forecasting, commodity markets

Thus, data scientists first determine the relevant data for training price forecasting machine learning models, where descriptive analytics plays an integral role.

The role of AI and Machine Learning in price forecasting

Artificial intelligence and machine learning offer businesses, regardless of size, extended capabilities by analysing and managing extensive datasets. This transformative impact naturally leads us to examine the specific benefits that AI and ML bring to the domain of price forecasting:

  • Pattern recognition: ML algorithms excel at identifying patterns and correlations in data that might not be obvious to human analysts. This can include seasonal trends, price elasticity, and consumer purchasing behaviours.
  • Real-time analysis: AI systems provide real-time analysis, which is important for dynamic pricing strategies in industries like retail, where prices may need to change rapidly in response to market conditions.
  • Industry-specific price forecasting: AI and ML models can be tailored to specific industries or products, allowing for more relevant price forecasting.
  • Market sentiment analysis: By using techniques like natural language processing, AI efficiently analyses news, social media, and other textual data to gauge market sentiment, which can influence price movements.
  • Continuous learning: ML models learn from new data and outcomes, constantly improving and refining their forecasting accuracy over time.
  • Error reduction: Automating the price prediction process reduces the scope for human error and bias. It also saves considerable time and effort previously spent on manual data analysis and forecasting.
price fluctuations - accurately forecast future values - stock prices - regression analysis - energy markets

Overall, AI and ML transform price forecasting into a more data-driven, precise, and efficient process, enabling businesses to make more informed decisions based on comprehensive market analysis.

What industries benefit the most from price prediction?

Price prediction models, fuelled by artificial intelligence, have become integral to the pricing strategies of numerous domains, including the following.

Travel industry

The travel industry, particularly airlines and hotels, is reaping significant benefits from artificial intelligence-powered price prediction.

By employing machine learning algorithms, these businesses accurately project future pricing trends.

With the help of machine learning models, companies use historical data and competitor pricing information, ensuring their price estimates are well-founded and competitive.

Creating competitive prices for seats or rooms that align with market sentiment can profoundly impact sales volume and profit margins.

The real-time outputs provided by machine learning algorithms ensure that these businesses consistently stay competitive.

Retail

Price prediction is equally vital in the retail industry. Retailers widely use price prediction models to set competitive prices with other stores, helping keep their products appealing.

Retailers can further employ these models to forecast customer satisfaction at various price points, enabling them to offer targeted discounts or coupons for specific items.

Machine learning models also can anticipate pricing trends over time, allowing retailers to adjust their strategies and thus optimise profits.

Predicting stock prices using AI

The stock market forecasting is characterised by its volatility and dynamism. The price forecasting becomes difficult due to the influence of numerous macro and micro factors, including political dynamics, global economic conditions, unforeseen events, and a company’s financial performance, among others.

The interest in employing machine learning for stock prediction is more than just a trend; it’s supported by concrete outcomes.

For example, the AI-powered hedge funds generated average returns of about 0.75% per month, vs. about 0.25% per month for the human-guided hedge funds.

This clearly demonstrates that using AI for stock prediction is a crucial factor in distinguishing between capitalizing on an opportunity and missing it in the rapidly changing financial landscape, where fortunes can be made or lost in a matter of seconds.

Data requirements for price forecasting

Every day, we generate approximately 328.77 million terabytes of data, a number that continues to grow due to our increasingly interconnected activities driven by global digitalization.

Big data ecosystems excel in capturing, storing, and managing these vast quantities of data, laying the groundwork for insightful analysis and value extraction.

Thus, businesses proactively use data to optimise their operations, reduce expenses, and boost profits.

However, data management is critically important for any business as it ensures the efficient and secure storage, organization, and retrieval of data, enabling informed decision-making and business operations.

forecasting models - past data - future price movements - statistical analysis - data points - term forecasting

The success of AI-powered solutions heavily relies on the quality of data they are trained and operate on. Poor data quality can lead to errors, biases, and unreliable outcomes. Improving data quality involves:

  • Establishing clear data governance policies
  • Conducting a data quality assessment
  • Data standardization and validation
  • Data cleansing
  • Implementing robust data integration
  • Monitoring data quality
  • Continuous improvement

 

Ample data can enable the training of more complex models. Yet, acquiring more data also increases the time, costs, and resources needed for its collection, storage, processing, and analysis.

Generally, more data results in more dependable models and improved outcomes, given the data is authentic and representative. It’s better to use a smaller amount of high-quality data than a larger volume of poor-quality data.

The bottom line

As the volume of generated data continues to grow, AI will increasingly become a valuable asset for businesses to maintain a competitive edge. By using AI and historical pricing data, companies can reveal insights beyond human capacity, enabling more informed decisions.

Also, we can now experiment with various scenarios and identify the most profitable price points while considering changes in costs, inventory, and pricing.

However, adopting new technologies is not enough. First of all, companies should implement a framework of procedures to regulate data management to secure the effectiveness of advanced analytics and AI solutions.

At Altamira, we provide Data Governance, Data Pipelines & MLOps services to help you prioritize and establish proper data governance practices needed for efficient price forecasting.

We also help businesses estimate market volatility, manage risks, and optimise their business strategies to steer dynamic markets with precision and confidence.

Be it stock price prediction using machine learning, energy resource optimization, or weather forecasting, predictive analytics is a new must. Contact us to learn how your business can benefit from price forecasting powered by AI!

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