Econometrics

Using Multivariate Time Series Forecasting with Transformers in Finance

In today’s fast-paced financial world, businesses require accurate and timely financial forecasting to make informed decisions. Multivariate time series forecasting has become a popular technique in finance to predict future values of multiple variables simultaneously. It is a statistical method that models the behavior of several interdependent variables over time, considering the historical data of …

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Leveraging SENet for More Accurate Financial Forecasting

Introduction Financial forecasting is a critical component of any business’s financial planning. The accuracy of these forecasts is essential for the decision-making process in the financial industry. In recent years, there has been a significant increase in the use of machine learning algorithms to make financial predictions. One such technology is SENet, which is being …

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The Top Multivariate Time Series Anomaly Detection Techniques Every Data Scientist Should Know

Introduction In today’s rapidly changing and highly complex world, anomaly detection has become an increasingly important aspect of data science. In many fields, including finance, cybersecurity, and healthcare, the ability to identify unusual patterns in data is crucial for making informed decisions. Multivariate time series anomaly detection, in particular, is a powerful technique for uncovering …

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50 Years of Progress: A Look Back at the Most Groundbreaking Statistical Advances

In the last 50 years, there has been a rapid growth in the field of statistics and its applications, which has led to a vast array of groundbreaking statistical advances. These advances have had a profound impact on many aspects of modern society, including business, healthcare, science, technology, and policy. In this post, we will …

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The Intersection of Machine Learning and Tail Risk Management in HFT

Introduction High-Frequency Trading (HFT) has transformed the financial markets, bringing increased liquidity and faster transaction times. However, it has also brought with it new and complex risks, particularly in the area of tail risk. Tail risk refers to the risk of rare and unexpected events that can have a significant impact on the market. In …

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From Outliers to Inliers: Robust Non-Parametric Regression with Median-of-Means

Regression analysis is a widely used statistical tool for predicting a continuous dependent variable based on one or more independent variables. However, traditional regression methods, such as linear and polynomial regression, can be sensitive to outliers and make incorrect predictions if the assumptions of normality and homoscedasticity are violated. To address these limitations, researchers have …

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Assessing Sovereign Risk in the Age of Deep Learning

Introduction Sovereign risk assessment is a critical component of modern finance, as it helps investors, analysts, and policy makers understand the potential risks and returns of investing in a particular country. In the past, sovereign risk assessment has been based on traditional methods such as credit ratings, macroeconomic indicators, and political risk analysis. However, with …

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Dynamic Factor Analysis vs Traditional Methods: Comparing the Accuracy of Inflation Forecasting

Introduction Inflation forecasting is a crucial task for policymakers and central banks as it helps in determining the appropriate monetary policy. The accuracy of inflation forecasting is of paramount importance, as it directly impacts the economy. In this blog post, we will compare the accuracy of dynamic factor analysis (DFA) and traditional methods in forecasting …

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