Forecasting methods in statistics
Pro Research Analysisby 
Searched over 200M research papers
Overview of Statistical Forecasting Methods
Statistical forecasting methods are essential tools for predicting future values based on historical data. The most widely used approaches include regression models, exponential smoothing, and stochastic time series models such as ARIMA (Autoregressive Integrated Moving Average). These methods can be tailored for both nonseasonal and seasonal time series, allowing for flexibility in various forecasting scenarios. There are also specialized models for handling extreme seasonality and hierarchical data structures, as well as techniques for managing outliers and disruptive events 13.
Choosing the Right Forecasting Method
Selecting the most appropriate forecasting method depends on the characteristics of the time series data. Research shows that the accuracy of different forecasting methods varies according to the properties of the data, such as trend, seasonality, and noise. Summary statistics derived from the data can help guide the selection process, but they may not always identify the single best method. Instead, they are useful for narrowing down to a set of good candidates .
Several strategies for selecting forecasting methods include convenience, market popularity, expert judgment, statistical criteria, track records, and guidelines from prior research. Quantitative methods generally outperform judgmental ones when sufficient data is available, while causal methods are better when large changes are expected. Simple methods are often preferred due to their ease of use and reliability .
Combining Forecasting Methods for Improved Accuracy
Combining forecasts from multiple methods can enhance accuracy and reduce risk. Theoretical and empirical studies show that combined forecasts often perform as well as or better than the best individual method, especially when the combining process is carefully managed. However, indiscriminate combining can sometimes degrade performance, so it is important to balance the potential gains with the complexity of the combination process 57. Automated combining methods can help achieve this balance and maintain strong performance.
Integrating Human Judgment with Statistical Forecasts
Human judgment still plays a valuable role in forecasting, especially when statistical models cannot capture all relevant information. Research indicates that forecasters can effectively select and adjust statistical forecasts based on visual records of past performance, and they tend to rely more on high-quality models as they gain experience . New approaches, such as "light-touch forecasting," combine human judgment with statistical algorithms to optimize the process, leveraging the strengths of both while minimizing inefficiencies 89.
Statistical vs. Machine Learning Forecasting Methods
While machine learning (ML) methods have gained popularity, studies comparing them to traditional statistical methods for time series forecasting have found that statistical methods generally outperform ML models in terms of accuracy and computational efficiency. ML models tend to require more resources and do not consistently deliver better results, especially for standard forecasting tasks. Objective and unbiased evaluations, such as large-scale forecasting competitions, are recommended to assess the true performance of different methods .
Conclusion
Statistical forecasting methods remain the backbone of predictive analytics, offering a range of models suited to different data types and forecasting needs. The choice of method should be guided by data characteristics, simplicity, and empirical evidence. Combining methods and integrating human judgment can further improve forecast accuracy. Despite advances in machine learning, traditional statistical approaches continue to provide reliable and efficient solutions for most forecasting applications.
Sources and full results
Most relevant research papers on this topic
Statistical methods for forecasting
This book provides a comprehensive introduction to statistical methods for forecasting nonseasonal and seasonal time series, highlighting the importance of using regression and exponential smoothing methods for accurate predictions.
DOI
Methods of Statistical Forecasting
This section provides practical extensions of forecasting methods, including combining forecasts, handling outliers, and using data mining techniques, to support management decision-making and enhance organizational performance.
DOI