Soft Commodity Tool: Time Series Forecasting On Seasonal Data

 Data is a new oil to the world that enables a soft commodity tool to forecast upcoming events by processing historical data. This tool uses time series forecasting techniques to transform seasonal data values into business intelligence dashboards.

These visualizations (Charts, Graphs, Dashboards) help stakeholders to draw valuable insights by building intuition with data points. Anyhow,  let’s understand what the phrase “soft commodities” means and how their accessibility impacts the common man's lifestyle.

What are Soft Commodities?

The term “Soft Commodities” refers to goods or things that people can exchange for their regular life consumption and grown through agriculture like wheat, maize, rice, cereals, etc. These soft commodities are consumed by living things with merely aiming to get energy. It’s hard to predict their prices, supply, and demand day-to-day. Therefore soft commodity tools are used.

Soft Commodities Tool for Time Series Forecasting

The time series forecasting technique is used when your dataset has a timestamp as a feature. Soft commodities tools such as Odyx yHat has a wide range of pre-build models on multiple time series algorithms including autoregressive moving average (ARMA), autoregressive integrated moving average, moving average, autoregression, and seasonal autoregressive integrated moving average.

Besides all, soft commodities tools also provide exploratory data analysis, feature engineering, data analytics, and visualizations. These features enable executives to greatly understand data patterns, meanings, and behavior of data points. In addition, it has countless capabilities as below.

How Do Soft Commodity Tools Help Businesses

These soft commodities tools enable commodity trading companies to predict their business volatility. Because volatility results in a loss of business capital and growth. Commodity forecasting tools are based on Artificial intelligence and Machine Learning so they make decisions based on historical data. Further, it has the following features to help companies.

• On-time risk mitigation

• Price volatility prediction

• Electricity load forecasting

• Better decision-making on real-time data

• Deep BI reports for business development teams

• Open new horizons to forecast future endeavors

In addition, these tools have the ability to process and predict data based on calendars like days, weeks, months, and years. For example, you have pharmaceutical data where you have to predict XYZ medicine supply and demand in a specific region or time. So, the time series algorithm will be applied for seasonal forecasting relative to timestamps.

Using this tool, data is processed, redundancies are eliminated, and supply and demand are forecasted. So, it will help to understand data patterns, data insights, and how it will be valuable for an organization's success by enhancing return on investment.

Conclusion

Time series forecasting tools such as Oydx yHat– Enable businesses to create deep data insights by converting them into business intelligence reports. The purpose of this tool is no code approach where you don’t need to onboard technical staff or hire companies to transform your raw data into an intelligible shape. In a quotation, it facilitates data owners from building data pipelines to drawing fruitful data insights.