Using Machine Learning to Improve Pricing Forecasts
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Using Machine Learning Algorithms to Predict Pricing Trends

Machine Learning Algorithms

Using Machine Learning Algorithms to Predict Pricing Trends

Price forecasting is a useful feature for consumers as well as businesses. A price prediction tool motivates users to engage with a brand or evaluate offers in order to spend their money wisely. Price prediction enables businesses to set pricing in a manner that builds customer engagement and loyalty. With Machine Learning (ML) technology a price prediction problem is formulated as a regression analysis which is a statistical technique used to estimate the relationship between a dependent/target variable and single or multiple independent (interdependent) variables. In regression, the target variable is numeric. This article will focus on ML algorithms used for price prediction in three different industries.


Airline and Hotel Industry

Airline and hotel prices fluctuate depending on season, specific days of the week, holidays, or other variables. For example, during the Christmas season the demand for hotels and flights increase and, as a result, prices skyrocket. Conversely, when a hotel struggles to fill its rooms, prices drop. A prime example is the effect of COVID-19. The pandemic has severely affected the airline and hospitality sector resulting in industry-wide price reductions in efforts to attract more customers.

Prices can also be negatively impacted if products and services are not continuously improved to keep pace with changing customer expectations. In addition, competitors might play a role by adjusting price strategies to gain a competitive advantage.


Effective ML Applications for the Airline and Hotel Industry

Recurrent neural network (RNN) or XGBoost are the most commonly used ML algorithms for flight and hotel price prediction. An RNN is a neural network for sequential data such as time series, text, video, speech, or financial data. Therefore, it can accurately forecast future values. Alternatively, XGBoost is a tree based supervised learning algorithm. It utilizes a set of weaker models (trees) to predict a target variable with more accuracy by averaging their estimates.

These ML algorithms can be used to predict airline and hotel prices for up to six months in advance—with a high degree of accuracy. This helps consumers decide whether to “buy now” or wait for a better deal.


Housing Industry

The attributes that directly affect housing prices include: number of bedrooms, construction year, construction quality, number of bathrooms, kitchen appliances, and many more. In addition, external factors such economic and political circumstances, interest rates, and local climate can also affect the housing market.

Other macro-level factors can include the Consumer Price Index (CPI), Gross Domestic Product (GDP) performance, unemployment rates, etc. These factors, when taken as a whole, can have a profound impact on local and regional housing markets.




Effective ML Applications for the Housing Industry

Researchers have used various combinations of regression trees, k-nearest neighbors, support vector machines, and deep networks for housing price prediction. Proper and intensive feature engineering is extremely important for improving prediction accuracy. Models comprised of regression tree ensembles predict prices with the highest rate of accuracy.

A complex ML algorithm is better than a linear regression model because the errors are substantially smaller. Time series modeling is also being used since it can greatly enhance a model’s prediction performance.


Stock Market

A stock buyer would like to reasonably decide when to buy stocks and when to sell them to make a profit. However, numerous factors such as supply and demand of stocks, global economic and political situations, historical prices, natural disaster, inflation, and deflation make it difficult to predict stock price patterns. Moreover, earnings per share (EPS), an indicator of a company’s performance, directly affects stock prices.

Stock prices can also experience unexpected highs and lows due to singular events that are very difficult for investors to foresee. This phenomenon, known as “market sentiment”, often results in investors making irrational investment decisions. To predict future stock price movement, Artificial Intelligence (AI) techniques can be applied using market and news data.


Effective ML Applications for Stocks

Time series modelling can be implemented to improve the ability to predict stock prices at a reasonable level of accuracy. A good ML and AI model looks at the history of a sequence of data and correctly predicts what the future elements of the sequence are going to be. For stock prices, there are no consistent patterns in the data to model stock prices over time. In this case, a model having 60% accuracy can deliver solid returns.

Advanced ML algorithms for stock prediction consist of time series forecasting algorithms such as Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving-Average (SARIMA), Vector Autoregression (VAR), and so on. The Long Short-term Memory (LSTM) method is a popular deep learning algorithm used for stock price prediction.

Research has also shown improved results using Support Vector Machine (SVM) with kernel trick.

As financial institutions begin to embrace AI technologies, ML approaches combined with other external factors such as news’ sentiments or a social media platform can be utilized to achieve higher degrees of accuracy in stock price prediction.

Price prediction is an important tool for customers, entrepreneurs, businesses, and property sellers. It can facilitate decision-making in everyday operations and in long-term planning. Assuming that a user has access to data that is current and of high quality, an ML algorithm can be utilized to provide valuable price forecast data. Choosing the right algorithm is essential since the algorithm must be able to capture the nature of the data and provide accurate estimates of the future price.