Artificial Intelligence (AI) Forecasting
Businesses today are eager to stay ahead and achieve faster growth, and relying solely on historical data for insights is no longer sufficient. To address this need, they must focus on forecasting future outcomes.
Traditional forecasting methods have leaned on statistical and mathematical techniques to predict future outcomes. However, these methods fall short when dealing with complex and dynamic data.
Given the vast amount of data modern businesses generate, they require modern technologies for forecasting. This is where AI forecasting comes into play.
What is AI forecasting?
AI planning and forecasting is the process of using artificial intelligence and machine learning techniques to predict future events. AI is adept at handling complex data at a large scale, yielding precise results efficiently.
Employing AI-powered models for forecasting can greatly benefit businesses as they can learn from historical data, identify patterns, and offer accurate predictions. With these insights, businesses can make informed decisions and strategize effectively.
Why is AI forecasting better than traditional forecasting methods?
AI-powered forecasting offers several advantages compared to traditional forecasting methods. Here's a comparison between traditional and AI forecasting methods.
| Traditional forecasting | AI forecasting |
|---|---|
| Struggles to handle complex data patterns | Can capture and model intricate and non-linear relationships in data. |
| Typically handles structured data | Can handle structured and unstructured data, large datasets, and high-dimensional data |
| May provide lower accuracy in capturing non-linear patterns | Offers higher accuracy as it can learn from data and model complex patterns. |
| Not well-suited for real-time forecasting | Can be configured for real-time forecasting |
| Subject to human bias in some cases | Predictions are completely data-driven |
| Limited scalability, especially with large or complex datasets | Can be scalable to accommodate growing datasets |
Now that you understand the major differences between traditional and AI forecasting methods, it's time to dive deep and understand various AI forecasting models.
What are some popular AI forecasting models & techniques?
Below are some of the popular AI forecasting models that help you get better forecasting results. Let's delve into each one to help you select the most suitable model for your needs:
- ARIMA (AutoRegressive Integrated Moving Average): The ARIMA model is preferred for handling time series data, which is an integration of autoregressive (AR) and moving average (MA) components.
- ETS (Error, Trend, Seasonality):The ETS model is also a time series forecasting technique that decomposes time series into three components: error, trend, and seasonality.
- STL (Seasonal-Trend decomposition using LOESS): The STL model decomposes time series data into three components: seasonal, trend, and residual. Loess is a method used for estimating non-linear relationships.
- Regression model:Regression models can also be used for time series data, but they are not exclusive. These models estimate relationships between a dependent variable and one or more independent variables.
Good for univariate time series data without trend and seasonality.
Use case:
Website traffic forecasting: Let's say you want to predict the website traffic over the next 3 months. ARIMA can be applied to capture trends and seasonality in website traffic, helping you make forecasts for marketing strategies.
Good for sales, demand forecasting, etc.
Use case:
Sales forecasting: ETS can be used to forecast the sales of a retail store. It helps in understanding the underlying trends, seasonal fluctuations, and errors in sales data, so you can optimize inventory and staff.
Good for data with strong seasonal and trend patterns like sales, weather, etc
Use case:
Sales forecasting: STL can be applied to forecast sales in a region. It helps in understanding the seasonal variations in purchase patterns.
Use case:
Marketing ROI analysis: Businesses can analyze the ROI for their marketing campaigns by using regression models to identify the marketing channels or strategies that drive the most sales and leads.
These are common AI forecasting models used by businesses for predictive analytics. The choice of a model depends on the type of data and the nature of the forecasting task. If you're unsure about which model suits your case best, you can leverage a modern BI and analytics platform like Zoho Analytics.
How does AI forecasting work?
AI forecasting leverages advanced machine learning algorithms to identify patterns and trends that can be used to make accurate predictions about future events or outcomes. The process typically involves:
- Data collection: Gathering relevant data from various sources. For example, sales records, market trends, economic indicators, and more.
- Data preparation: Cleaning and formatting the data to prepare it for analysis, handling missing values, and removing outliers.
- Model training: Using machine learning techniques like regression or ARIMA to train a model on the data. This model training helps learn the underlying patterns and relationships.
- Forecasting: Applying the trained model to the data to generate forecasts or predictions about future events or outcomes.
Benefits of AI forecasting & planning
AI forecasting offers several significant benefits for businesses and organizations:
- Improved accuracy: AI forecasting models can analyze a huge volume of data from multiple sources, which would be difficult or impossible for humans to discern. AI forecasting systems can also provide more accurate predictions than traditional forecasting methods.
- Increased efficiency: Manual forecasting processes can be time-consuming, labor-intensive, and prone to errors. AI forecasting automates much of the data collection, preprocessing, and modeling tasks, significantly improving efficiency.
- Faster & strategic decision-making: AI forecasting systems can process large datasets and generate forecasts in real-time, providing decision-makers with up-to-date insights. This timely access to accurate forecasts enables faster decision-making.
- Better insights: AI forecasting models can uncover hidden patterns, trends, and correlations that may not be immediately apparent to human analysts. By leveraging advanced techniques like machine learning, natural language processing, and deep learning, AI forecasting systems can provide deeper insights and nuanced understanding of complex data.
- Competitive advantage: Organizations that effectively implement AI forecasting and planning gain a significant competitive advantage. With more accurate forecasts, faster decision-making, and deeper insights, these organizations can better anticipate market shifts, optimize resource allocation, and respond to customer needs more effectively than their competitors.
Zoho Analytics for AI forecasting
Zoho Analytics supports all the above-mentioned AI forecasting models for predictive analytics. With Zoho Analytics as your BI and analytics platform, you won't need to worry about selecting an AI forecasting model for predictive analytics. The platform automatically chooses the most suitable model based on your data type.
However, if you have a particular model in mind, you can select it to conduct your analysis.
Here's an example of a revenue forecast for an e-commerce company:
AI forecasting empowers you to predict sales or marketing ROI for the upcoming months based on your past performance. Furthermore, AI enables what-if analysis through a cause-and-effect model, allowing you to simulate variables effortlessly. You can even specify a confidence range for your forecasted data based on your chosen confidence level.
Below is a sample report that forecasts future sales using what-if forecasting. You can adjust the sales budget increase percentage with a slider and see the forecasted sales.
This report allows you to simulate different advertising budgets and forecast your sales for the months ahead. AI analytics equips businesses to forecast future trends with greater precision, enabling smarter decision-making and proactive growth planning.
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