HSE Researchers Demonstrate Effectiveness of Machine Learning in Forecasting Inflation
Inflation is a key indicator of economic stability, and being able to accurately forecast its levels across regions is crucial for governments, businesses, and households. Tatiana Bukina and Dmitry Kashin at HSE Campus in Perm have found that machine learning techniques outperform traditional econometric models in long-term inflation forecasting. The results of the study focused on several regions in the Privolzhskiy Federal District have been published in HSE Economic Journal.
Inflation forecasting is crucial for the economy, particularly since Russia's transition to an inflation targeting regime in 2014. This means that the Bank of Russia sets specific inflation targets and employs a range of tools to meet them.
Various data is used to predict inflation, including the consumer price index, the unemployment rate, exchange rates, and the Central Bank rate. To systematise this information for forecasting, economists at the HSE Campus in Perm used data from the Unified Interdepartmental Statistical Information System.
The researchers' main objective was to determine which model predicts regional inflation more accurately: traditional econometric time series models or more recent machine learning methods. The study analysed data from 14 regions in the Privolzhskiy Federal District from January 2010 to December 2022. R Studio and Python were used for the analysis: time series forecasting was performed in R Studio, while machine learning models, including support vector machines, gradient boosting, and random forests, were implemented in Python. The forecasts were conducted on test samples, which helped prevent model overfitting and provided more accurate estimates.
The authors employed a cross-validation method using test samples of equal size. This approach allows models to be trained on data from one period and tested on data from another, ensuring stability and accuracy of the forecasts.
'To ensure accurate performance of machine learning methods, it is essential to select the optimal hyperparameters for the models. Hyperparameters differ from other model parameters in that they are set before training begins and define the model's specifications. Cross-validation is employed to select the optimal hyperparameters. When cross-validating time series, the training data precedes the test data without overlap, unlike in standard data validation,' according to Tatiana Bukina, Associate Professor, Faculty of Computer Science, Economics, and Social Sciences, HSE Campus in Perm.
The study found the gradient boosting model to be the most accurate of all machine learning models considered for predicting regional inflation. It delivers more accurate forecasts than autoregressive models over more time horizons. Thus, at forecasting horizons of 3, 6, 21, and 24 months, the gradient boosting model outperforms the basic AR(1) model by 20.3%, 16.2%, 72.5%, and 77.7%, respectively. The AR(1) model, a statistical tool for analysing and predicting time series, assumes that the current value of a series depends on its previous value plus a random error.
The random forest model and the support vector machine also demonstrated accurate forecasts over the long horizons of 21 and 24 months, outperforming the AR(1) model by 72.5% and 77.7%, respectively. A random forest combines multiple decision trees to enhance the accuracy and stability of forecasts, and then uses regression to average the predictions or select the most frequent value. The support vector machine identifies the optimal line that separates the data while minimising classification errors.
According to the authors, their results confirm that machine learning methods can be effective for forecasting inflation across various time horizons.
Tatiana Bukina notes, 'Our research has demonstrated that machine learning provides more reliable tools for long-term forecasts. However, traditional econometric models continue to play a crucial role in short-term forecasts and should not be entirely excluded from analysts' toolkits. Combining econometric modelling with machine learning methods can significantly improve the accuracy of regional inflation forecasts. This is particularly important in an environment characterised by high uncertainty and rapid changes in economic conditions.'
The study also highlighted the specific characteristics of inflation forecasting for different regions. For example, in machine learning models, inflation seasonality was observed only in the Perm, Nizhny Novgorod, Penza, and Saratov regions. In the Republic of Tatarstan, the specific month for which the forecast was calculated proved to be a significant factor.
In the random forest model, the average inflation value for the previous three months emerged as a significant factor for the Republic of Mordovia, Nizhny Novgorod and Ulyanovsk regions, and the Chuvash Republic.
Each region has unique characteristics related to its economic structure, natural resources, and geographical location. These factors account for the variations in inflation dynamics and key macroeconomic indicators.
See also:
Machine Learning Links Two New Genes to Ischemic Stroke
A team of scientists from HSE University and the Kurchatov Institute used machine learning methods to investigate genetic predisposition to stroke. Their analysis of the genomes of over 5,000 people identified 131 genes linked to the risk of ischemic stroke. For two of these genes, the association was found for the first time. The paper has been published in PeerJ Computer Science.
First Digital Adult Reading Test Available on RuStore
HSE University's Centre for Language and Brain has developed the first standardised tool for assessing Russian reading skills in adults—the LexiMetr-A test. The test is now available digitally on the RuStore platform. This application allows for a quick and effective diagnosis of reading disorders, including dyslexia, in people aged 18 and older.
Low-Carbon Exports Reduce CO2 Emissions
Researchers at the HSE Faculty of Economic Sciences and the Federal Research Centre of Coal and Coal Chemistry have found that exporting low-carbon goods contributes to a better environment in Russian regions and helps them reduce greenhouse gas emissions. The study results have been published in R-Economy.
Russian Scientists Assess Dangers of Internal Waves During Underwater Volcanic Eruptions
Mathematicians at HSE University in Nizhny Novgorod and the A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences studied internal waves generated in the ocean after the explosive eruption of an underwater volcano. The researchers calculated how the waves vary depending on ocean depth and the radius of the explosion source. It turns out that the strongest wave in the first group does not arrive immediately, but after a significant delay. This data can help predict the consequences of eruptions and enable advance preparation for potential threats. The article has been published in Natural Hazards. The research was carried out with support from the Russian Science Foundation (link in Russian).
Centre for Language and Brain Begins Cooperation with Academy of Sciences of Sakha Republic
HSE University's Centre for Language and Brain and the Academy of Sciences of the Republic of Sakha (Yakutia) have signed a partnership agreement, opening up new opportunities for research on the region's understudied languages and bilingualism. Thanks to modern methods, such as eye tracking and neuroimaging, scientists will be able to answer questions about how bilingualism works at the brain level.
How the Brain Responds to Prices: Scientists Discover Neural Marker for Price Perception
Russian scientists have discovered how the brain makes purchasing decisions. Using electroencephalography (EEG) and magnetoencephalography (MEG), researchers found that the brain responds almost instantly when a product's price deviates from expectations. This response engages brain regions involved in evaluating rewards and learning from past decisions. Thus, perceiving a product's value is not merely a conscious choice but also a function of automatic cognitive mechanisms. The results have been published in Frontiers in Human Neuroscience.
AI Predicts Behaviour of Quantum Systems
Scientists from HSE University, in collaboration with researchers from the University of Southern California, have developed an algorithm that rapidly and accurately predicts the behaviour of quantum systems, from quantum computers to solar panels. This methodology enabled the simulation of processes in the MoS₂ semiconductor and revealed that the movement of charged particles is influenced not only by the number of defects but also by their location. These defects can either slow down or accelerate charge transport, leading to effects that were previously difficult to account for with standard methods. The study has been published in Proceedings of the National Academy of Sciences (PNAS).
Electrical Brain Stimulation Helps Memorise New Words
A team of researchers at HSE University, in collaboration with scientists from Russian and foreign universities, has investigated the impact of electrical brain stimulation on learning new words. The experiment shows that direct current stimulation of language centres—Broca's and Wernicke's areas—can improve and speed up the memorisation of new words. The findings have been published in Neurobiology of Learning and Memory.
Artificial Intelligence Improves Risk Prediction of Complex Diseases
Neural network models developed at the HSE AI Research Centre have significantly improved the prediction of risks for obesity, type 1 diabetes, psoriasis, and other complex diseases. A joint study with Genotek Ltd showed that deep learning algorithms outperform traditional methods, particularly in cases involving complex gene interactions (epistasis). The findings have been published in Frontiers in Medicine.
Cerium Glows Yellow: Chemists Discover How to Control Luminescence of Rare Earth Elements
Researchers at HSE University and the Institute of Petrochemical Synthesis of the Russian Academy of Sciences have discovered a way to control both the colour and brightness of the glow emitted by rare earth elements. Their luminescence is generally predictable—for example, cerium typically emits light in the ultraviolet range. However, the scientists have demonstrated that this can be altered. They created a chemical environment in which a cerium ion began to emit a yellow glow. The findings could contribute to the development of new light sources, displays, and lasers. The study has been published in Optical Materials.