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A forecast is a prediction made by studying historical data and past patterns. Businesses use software tools and systems to analyze large amounts of data collected over a long period. The software then predicts future demand and trends to help companies make more accurate financial, marketing, and operational decisions.
Why is forecasting important?
Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future. It helps managers respond confidently to changes, control business operations, and make strategic decisions that drive future growth. For example, businesses use forecasting to do the following:
- Use resources more efficiently
- Visualize business performance
- Time the launch of new products or services
- Estimate recurring costs
- Predict future events like sales volumes and earnings
- Review management decisions
What are the types of forecasting methods?
Forecasting methods can be qualitative or quantitative:
Qualitative methods
Qualitative forecasting relies on marketing experts to make short-term predictions. You can use qualitative methods when there is insufficient historical data. For example, these are two use cases:
- Market research techniques like polls and surveys identify consumer demand.
- Delphi modeling techniques poll experts in a particular field to collect their opinions and predict trends in that field.
Quantitative methods
Quantitative forecasting models use meaningful statistics and historical data to predict long-term future trends. We give examples of standard quantitative methods below:
- Econometric modeling analyzes financial data sets, like loan and investment data, to predict significant economic shifts and their impact on the company.
- The indicator approach compares data points to identify relationships between seemingly unrelated data. For example, you can use changes in GDP to forecast unemployment rates.
- In this scenario, GDP data is called the lead indicator, and the unemployment rate is the lagging indicator.
- Time-series forecasting analyzes data collected over different intervals of time to predict future trends.
What is time-series data?
Cross-sectional data observes individuals and companies in the same time period. On the other hand, time-series data is any data set that collects information at various time intervals. This data is distinct because it orders data points by time. As a result, there is potential for correlation between observations in adjacent intervals.
Time-series data is plottable on a graph with incremental intervals (or timelines) on the x-axis and observed sample data values on the y-axis. Such time-series graphs are valuable tools for visualizing the data. Data scientists use them to identify forecasting data characteristics. We give some examples of time-series data characteristics below:
Time trending data
In trending data, y-values increase or decrease with time, making the graph appear linear. For example, population data may increase or decrease linearly with time.
Seasonality
Seasonal patterns occur when time-series data shows regular and predictable patterns at time intervals of less than a year. This data pattern may appear as spikes or other anomalies on an otherwise linear graph. For example, a store’s retail sales might increase in the holiday periods around December and April.
Structural breaks
Sometimes time-series data suddenly changes behavior at a certain point in time. The time-series graph may suddenly shift up or down, creating a structural break or non-linearity. For instance, many economic indicators changed sharply in 2008 after the start of the global financial crisis.
What is time-series forecasting?
Time-series forecasting is a data science technique that uses machine learning and other computer technologies to study past observations and predict future values of time-series data. Let’s look at some examples of time-series forecasting:
- Astronomical data consists of repetitive movements of the planets over centuries. You can use this data to predict astronomical events like eclipses and comets accurately.
- Weather forecasting uses wind and temperature patterns to predict weather changes.
- Scientists can use birth rates and migration data to predict population growth.
Time-series analysis vs. time-series forecasting
Time-series analysis explores the underlying causes in any time-series data. This field of study seeks to understand the “why” behind a time-series dataset. Analysts must often make assumptions and decompose or break down the data to extract meaningful statistics and other characteristics.
While time-series analysis is all about understanding the dataset, forecasting is all about predicting it. These are the three steps of predictive modeling:
- Ask a question and collect a sample set of time-series data that answers this question for a past time period.
- Train the computer software or forecasting algorithm using the past values.
- Use the forecasting algorithm to make future observations.
How does time-series forecasting work?
Data scientists use time-series forecasting models to make more accurate predictions. They first do some exploratory data analysis to select the best forecasting algorithms, and then use machine learning models to make predictions. Let’s look at some common forecast models below:
Decomposition models
Decomposition models decompose or break down time-series data into three components:
- Trend component
- Seasonal component
- Noise component, which does not belong to either of the above two groups
Another method of analyzing time-series data is to break it down into two components: predictable and unpredictable data components.
Smoothing-based models
Data smoothing is a statistical technique that involves removing outliers or data points that differ significantly from the rest of the data set. These forecasting models make the underlying pattern category more visible by eliminating random variations in data.
Regression-based models
Autoregression is a forecasting model that uses observations from previous time steps to define a mathematical relationship between two data points. It then uses the mathematical relationship to estimate an unknown future value. Depending on the regression model being used, the mathematical equation considers past forecast errors and seasonal past values, improving the prediction over time.
What are key use cases for forecasting?
Forecasting gives businesses relevant and reliable information about both the present and the future. We describe some example use cases of forecasting technology below:
Operations – How More Retail Limited uses automation to forecast product sales?
More Retail Ltd. (MRL) is one of India’s top four grocery retailers, with several billion dollars in revenue. They have an extensive store network and a complex supply chain of distributors. They were relying on the manual judgment of store managers to estimate and order stock, but this affected customer experience, especially in the fresh produce category. MRL used forecasting services by AWS to build an automated ordering system that reduced fresh food wastage by 30%.
Manufacturing – How Foxconn uses forecasting to manage manufacturing demand?
Hon Hai Technology Group (Foxconn) is the world’s largest electronics manufacturer and solutions provider. During the COVID-19 pandemic, Foxconn faced unprecedented volatility in customer demand, supplies, and capacity. The company collaborated with the Amazon Machine Learning Solutions Lab to predict accurate net order forecasts for their factory in Mexico. These forecasts led to an annual savings of over $500,000.
Customer support – How Affordable Tours uses sales forecasting to improve customer experience?
Affordable Tours.com is one of the largest vendors of escorted tours, cruises, river cruises, and active vacations in the United States. They were struggling to allocate resources when handling customer call volumes. Some days they had too many agents, and other days they had too few, which created inconsistent customer experiences and increased missed call rates. They used Amazon Forecast to anticipate customer call volumes better and improved their missed call rate by 20%.
