The ability to predict commodity demand and prices is the key to safeguarding your business from the risks associated with volatility that we discussed in the last blog . This blog evaluates how AI / ML-enabled time series based forecasting can mitigate the risks associated with commodities.
Many prediction problems, including commodity price forecasting, involve a time component and thus require the extrapolation of time-series data or time series forecasting. Let us explore what makes time-series unique among the other prediction problems.
Time series forecasting is a technique for predicting events through a sequence of time. It predicts future events by analyzing the past trends on the assumption that future trends and correlations will hold similar to historical trends.
The business world has been using traditional statistical modelling for predicting one or two key business metrics for a long time. Time-series forecasting is one of the most applied data science techniques in business, finance, supply chain management, production and inventory planning.
Firms have been using statistical models to generate a static forecast for a specific period, usually static estimates for the following year. That doesn’t provide the required accuracy or the agility necessary for taking decisions in today’s business world. Further, the complexity of today’s business and the influence of multiple variables, sometimes running into 1000’s, may limit the firm’s ability to make accurate predictions. Finally, companies need more up to date forecasting, taking into account the constantly changing macroeconomic factors, to stay ahead of the competition.
AI/ML complements traditional time series statistical modelling. Together they form a very powerful engine that can take in thousands of variables and provide more accurate forecasts. They can be continuously updated based on the most up-to-date information from the market; it is known as „nowcasting“.
Time-series forecasting is one of the most challenging topics in the AI/ML area. Among the factors that make time series forecasting challenging, the most important ones are:
1. Time Dependence of Time-Series: The basic assumption of a linear regression model that the observations are independent doesn’t hold in this case. Time-series data is correlated over time; forecasting cannot rely on usual regression techniques. Training data sets should contain observations before validation sets to avoid biased evaluations. Once we have chosen the best model based on the training data, the performance is evaluated on a separate test set subsequent in time.
2. Seasonality & Trends in a Time-Series: Along with an increasing or decreasing trend over time, most time series also have some form of seasonal trends, i.e. variations specific to a particular time frame. For example, sales of ice cream increase in summer compared to winter.
The best time-series AI/ML model suited for your dataset may differ. It depends on the type of dataset, forecasting horizon, size and granularity of the data set. Therefore, specific time series models outperform others on a particular dataset. However, they may provide the worst accuracy for another dataset. Therefore, developing an AI/ML-powered time series model best suited for your prediction problem is a huge undertaking. But if done correctly, it can give forecasts that can be more than 80% accurate.
An AI/ML-powered forecasting platform can also take in as many relevant business indicators as possible to generate the most accurate forecasts possible. It doesn’t matter if you have 100 or 100,000 factors that could influence your predictions; Machine Learning can find patterns and correlations in data that a traditional (or human) system would never be able to find. Along with these indicators, a pool of fine-tuned models across AI, ML and statistical modelling and an automatic selection of the best model based on data can help improve the accuracy dramatically.
Time-series AI/ML can be applied to forecast commodity prices by considering all the variables that matter. These variables include supply factors, demand factors, transportation costs, production costs, essential raw material costs, and the sentiment in the market. A great NoCode AI solution mobilizes the entire machine learning process for you. From selecting the relevant data and models to training the model, a "NoCode" solution provides you with the forecasts for consumption without the hassle of contacting data scientists or IT departments or calling experts. A "NoCode" solution means - you no longer need to rely on gut feeling or data scientists or experts while taking those multi-million dollar decisions on commodity purchase .