Forecasting is the process of predicting future events or trends based on historical data and patterns.
Forecasting plays a crucial role in various fields, such as finance, economics, and energy management. Machine learning techniques have been increasingly employed to improve the accuracy and reliability of forecasts. Recent research in this area has focused on developing new methods and models to enhance forecasting performance.
One approach to improve forecasting accuracy is by combining multiple models, known as forecast combinations or ensembles. This method helps mitigate the uncertainty associated with selecting a single 'best' forecast. Factor Graphical Model (FGM) is a novel approach that separates idiosyncratic forecast errors from common errors, leading to more accurate combined forecasts.
Probabilistic load forecasting (PLF) is another area of interest, as it provides uncertainty information that can improve the reliability and economics of system operation performances. A two-stage framework has been proposed that integrates point forecast features into PLF, resulting in more accurate hour-ahead load forecasts.
Nonlinear regression models have also been used to forecast air pollution levels, such as PM2.5 concentration. These models can provide accurate next-day forecasts and efficiently predict high-concentration and low-concentration days.
In addition to these methods, researchers have explored rapid adjustment and post-processing of temperature forecast trajectories, creating probabilistic forecasts from deterministic forecasts using conditional Invertible Neural Networks (cINNs), and evaluating the information content of DSGE (Dynamic Stochastic General Equilibrium) forecasts.
Practical applications of these forecasting techniques include:
1. Energy management: Accurate load forecasting can help utility companies optimize power generation and distribution, leading to more efficient and reliable energy systems.
2. Environmental monitoring: Forecasting air pollution levels can inform public health policies and help authorities implement timely measures to mitigate the impact of poor air quality.
3. Economic planning: Accurate macroeconomic forecasts can guide policymakers in making informed decisions regarding fiscal and monetary policies.
A company case study in this context is the use of particle swarm optimization (PSO) for multi-resolution, multi-horizon distributed solar PV power forecasting. This approach combines the forecasts of multiple models, resulting in more accurate predictions for various resolutions and horizons. The PSO-based forecast combination has been shown to outperform individual models and other combination methods, making it a valuable tool for solar forecasters.
In conclusion, machine learning techniques have significantly advanced the field of forecasting, offering more accurate and reliable predictions across various domains. By connecting these methods to broader theories and applications, researchers and practitioners can continue to develop innovative solutions to complex forecasting challenges.
Forecasting
Forecasting Further Reading
1.Learning from Forecast Errors: A New Approach to Forecast Combinations http://arxiv.org/abs/2011.02077v2 Tae-Hwy Lee, Ekaterina Seregina2.Probabilistic Load Forecasting via Point Forecast Feature Integration http://arxiv.org/abs/1903.10684v1 Qicheng Chang, Yishen Wang, Xiao Lu, Di Shi, Haifeng Li, Jiajun Duan, Zhiwei Wang3.Nonlinear regression models to forecast PM$_{2.5}$ concentration in Wuhan, China http://arxiv.org/abs/2302.14505v1 Jinghong Zeng4.Rapid adjustment and post-processing of temperature forecast trajectories http://arxiv.org/abs/1910.05101v1 Nina Schuhen, Thordis Thorarinsdottir, Alex Lenkoski5.Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks http://arxiv.org/abs/2302.01800v1 Kaleb Phipps, Benedikt Heidrich, Marian Turowski, Moritz Wittig, Ralf Mikut, Veit Hagenmeyer6.Information Content of DSGE Forecasts http://arxiv.org/abs/1808.02910v1 Ray Fair7.Multi-Resolution, Multi-Horizon Distributed Solar PV Power Forecasting with Forecast Combinations http://arxiv.org/abs/2206.10795v1 Maneesha Perera, Julian De Hoog, Kasun Bandara, Saman Halgamuge8.Evaluating Forecasts with scoringutils in R http://arxiv.org/abs/2205.07090v1 Nikos I. Bosse, Hugo Gruson, Anne Cori, Edwin van Leeuwen, Sebastian Funk, Sam Abbott9.Another look at forecast trimming for combinations: robustness, accuracy and diversity http://arxiv.org/abs/2208.00139v1 Xiaoqian Wang, Yanfei Kang, Feng Li10.Inter Time Series Sales Forecasting http://arxiv.org/abs/1303.0117v1 Manisha GahirwalForecasting Frequently Asked Questions
What do you mean by forecasting?
Forecasting is the process of predicting future events or trends based on historical data and patterns. It is widely used in various fields, such as finance, economics, and energy management, to make informed decisions and plan for the future. Machine learning techniques have been increasingly employed to improve the accuracy and reliability of forecasts by developing new methods and models.
What are the three types of forecasting?
The three main types of forecasting are: 1. Time series forecasting: This type of forecasting uses historical data to predict future values of a variable. It is based on the assumption that past patterns will continue into the future. Examples include predicting stock prices, sales, and energy demand. 2. Causal forecasting: This type of forecasting identifies relationships between variables and uses these relationships to make predictions. It assumes that changes in one variable cause changes in another variable. Examples include predicting the impact of marketing campaigns on sales or the effect of weather on energy demand. 3. Qualitative forecasting: This type of forecasting relies on expert opinions, surveys, and other subjective methods to predict future events or trends. It is often used when historical data is limited or unavailable. Examples include predicting new product demand or forecasting political events.
What is forecasting in business?
In business, forecasting is the process of predicting future events, trends, or outcomes based on historical data, patterns, and expert opinions. It helps businesses make informed decisions, allocate resources efficiently, and plan for the future. Accurate forecasting can lead to better financial performance, improved customer satisfaction, and increased competitiveness. Examples of business forecasting include sales forecasting, demand forecasting, and financial forecasting.
What is an example of forecasting?
An example of forecasting is predicting the demand for a product in the upcoming months based on historical sales data, seasonal patterns, and market trends. This information can help a company plan its production, inventory management, and marketing strategies to meet the anticipated demand and maximize profits.
How do machine learning techniques improve forecasting?
Machine learning techniques improve forecasting by automatically identifying patterns and relationships in historical data, allowing for more accurate and reliable predictions. These techniques can adapt to new data and changing conditions, making them well-suited for forecasting tasks. Recent research in this area has focused on developing new methods and models, such as forecast combinations, probabilistic load forecasting, and nonlinear regression models, to enhance forecasting performance.
What are some practical applications of forecasting techniques?
Practical applications of forecasting techniques include: 1. Energy management: Accurate load forecasting helps utility companies optimize power generation and distribution, leading to more efficient and reliable energy systems. 2. Environmental monitoring: Forecasting air pollution levels can inform public health policies and help authorities implement timely measures to mitigate the impact of poor air quality. 3. Economic planning: Accurate macroeconomic forecasts can guide policymakers in making informed decisions regarding fiscal and monetary policies. 4. Supply chain management: Demand forecasting helps businesses plan production, inventory management, and distribution strategies to meet customer needs and minimize costs.
What is a forecast combination or ensemble?
A forecast combination or ensemble is a method that combines multiple forecasting models to improve accuracy and mitigate the uncertainty associated with selecting a single 'best' forecast. By leveraging the strengths of different models and accounting for their individual errors, forecast combinations can often provide more accurate and reliable predictions than any single model alone.
What is probabilistic load forecasting (PLF)?
Probabilistic load forecasting (PLF) is an approach that provides uncertainty information along with point forecasts for energy demand. This information can improve the reliability and economics of system operation performances by helping decision-makers account for potential variations in demand. PLF techniques often involve the use of machine learning models, such as two-stage frameworks that integrate point forecast features into the probabilistic forecasting process.
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