A new extension of random forest technique for vector autoregressive model

Various analytical techniques are available for modeling multivariate time series data. One such approach is the State Space Model, which can be employed to model this type of data. In this study, the data to be analyzed are data on the Indonesia Rupiah (IDR) exchange rate (ExR) against the US Dollar (USD), oil and gas exports (OGE), money supply (MS) and non-oil and gas exports (non-OGE) from January 2008 to December 2019. The aim of this study is to identify the most suitable state space model for the given data. In this research, the state space method will be applied to multivariate time series data, with the state space represented in the Vector Autoregressive (VAR) model to explore the interrelationships among groups of observed variables. The VAR model is a statistical technique used to analyze the relationships between variables in the dataset, employing the Granger Causality Test. The state space model is utilized to model and forecast multiple interconnected time series, where the variables exhibit dynamic interactions and to examine additional unobserved variables in the time series data. Based on the analysis results and the minimum value of the Akaike Information Criterion (AIC), the optimal VAR model identified is the VAR (6) model. The results of forecasting values using the state space model show that the predicted values and the real values for the state space model are very closed to each other.

SUMMARY & CONCLUSIONSModeling corrosion growth for complex systems such as the oil refinery system is a major challenge since the corrosion process of oil and gas pipelines are inherently stochastic and depends on many factors including exposures to environmental conditions, operating conditions, and electrochemical reactions. Moreover, the number of sensors is usually limited, and sensor data are incomplete and scattering, which hinders the capability of capturing the corrosion growth behaviors. Therefore, this paper proposes Multi-sensor Corrosion Growth Model with Latent Variables to predict the corrosion growth process in oil refinery piping. The proposed model is a combination of the hierarchical clustering algorithm and the vector autoregression (VAR) model. The clustering algorithm aims to find the hidden (i.e., latent) data clusters of the measured time series data, from which the time series from the same cluster will be included in the VAR model to predict the corrosion depth from multiple sensors. The model can capture the relationship between sensor time series data and identify latent variables. A real case study of an oil refinery system, in which in-line inspection (ILI) data were collected, was utilized to validate model. Regarding corrosion growth prediction, the paper compared the prediction accuracy of VAR model with other three forms of power law model, which is widely accepted to expect the time-dependent depth of corrosion such as power function (PF), PF with initiation time of corrosion (PFIT), and PF with initiation time of corrosion and covariates (PFCOV). The results showed that VAR model has the lowest prediction error based on the mean absolute percentage error (MAPE) evaluation for test data. Finally, the proposed model is believed to be useful for dealing with a complex system that has a variety of corrosion growth behaviors, such as the oil refinery system, as well as it can be applied in other real-time applications.

A multi-granularity hierarchical network for long- and short-term forecasting on multivariate time series data

The development of advanced recording and measurement devices in scientific fields is producing high-dimensional time series data. Vector autoregressive (VAR) models are well suited for inferring Granger-causal networks from high dimensional time series data sets, but accurate inference at scale remains a central challenge. We have recently introduced a flexible and scalable statistical machine learning framework, Union of Intersections (UoI), which enables low false-positive and low false-negative feature selection along with low bias and low variance estimation, enhancing interpretation and predictive accuracy. In this paper, we scale the UoI framework for VAR models (algorithm UoI V AR ) to infer network connectivity from large time series data sets (TBs). To achieve this, we optimize distributed convex optimization and introduce novel strategies for improved data read and data distribution times. We study the strong and weak scaling of the algorithm on a Xeon-phi based supercomputer (100,000 cores). These advances enable us to estimate the largest VAR model as known (1000 nodes, corresponding to 1M parameters) and apply it to large time series data from neurophysiology (192 neurons) and finance (470 companies).

An empirical analysis of causal nexus between higher education and economic growth in BRICS countries

Accurate forecasting of agricultural commodity prices, particularly vegetables, is essential for comprehending market dynamics and maintaining economic stability among stakeholders. Effective price forecasting fosters agricultural sustainability and economic resilience. This study aims to identify the optimal model for forecasting wholesale eggplant prices in the Lucknow market, comparing the traditional vector autoregressive (VAR) model with the Artificial Neural Network (ANN) model. The most commonly used evaluation metrics were applied to compare and assess model performance. For model building, 204 monthly observations from January 2008 to December 2024, comprising a dataset of wholesale prices and total arrival of eggplant in the Lucknow center, were utilized. In the modeling process, the entire dataset was partitioned into two subsets, training and testing data, with a split of 80:20. To check stationarity, the Augmented Dickey-Fuller test was conducted, and the lag order for the VAR model was selected based on the minimum AIC value, with [5] as the optimal lag order. Similarly, the ANN model was created using [5] lags of the variables, as independent variables. The findings indicate that the ANN [10: 3: 2: 1] model surpasses the VAR (5) model in forecasting wholesale prices within the test dataset. However, both models exhibit overfitting, likely attributable to the VAR model's inadequacy in capturing the heteroscedasticity effect and the ANN model's dependence on a limited dataset and the number of lag variables incorporated as inputs. These methods can be applied to model and forecast prices of other agricultural commodities and have potential in broader agricultural research, as their utilization in this field remains limited.

In this paper, we have estimated vector autoregression (VAR), Bayesian vector autoregression (BVAR) and vector error-correction models (VECMs) using annual time-series data of South Korea for 1950-94. We find evidence supporting the view that growth of real per-capita income has been aided by income, investment and export growth, as well as government spending and exchange rate policies. The VECMs provide better forecasts of growth than do the VAR and BVAR models for both short-term and long-term predictions.

PT Perkebunan Nusantara VIII (PTPN VIII) is a State-Owned Enterprise (BUMN). It operates in the plantation sector. The leading commodity is tea. The demand for tea produced by PTPN VIII is increasing. Thus, planning tea production is necessary. One of the production planning efforts is through forecasting based on previous data. Tea production data is time-series data. It contains seasonal elements and is dependent on other locations. We will analyze data with these criteria using space-time models, one of which is vector autoregressive (VAR). VAR models the relationship between observations on certain variables at one time. It also models the observation of the variable itself at previous times. Additionally, VAR models the relationship between observations and other variables at previous times. This paper explains how to forecast tea production. It uses the reconstituted VAR and Seasonal Autoregressive Moving Average (SARIMA) models. The results showed that the reconstituted VAR model was better than the SARIMA model in predicting tea production. The tea production prediction was at the Sedep and Santosa plantations in Bandung Regency.

ObjectivesInternet search engine data have been widely used to monitor and predict infectious diseases. Existing studies have found correlations between search data and HIV/AIDS epidemics. We aimed to extend the...

The speed of China’s economic development is gradually accelerating, and the demand for energy is also constantly increasing, especially the demand for coal. In order to reveal whether the coal imports have an impact on China’s economic development, this paper constructs the VAR(6) model by selecting the quarterly data of coal imports (CIV) and gross domestic product (GDP) from 2002 to 2017, performing ADF (Augmented Dickey-Fuller) stationarity test and Johansen cointegration test. It shows that there is a long-term stable equilibrium relationship between coal imports and GDP. Then the impulse response function is used to obtain the relationship between coal imports and GDP. It is found that the impact of coal imports on GDP is greater than the impact of GDP on coal imports.

Bayesian non-parametric methods based on Dirichlet process mixtures have seen tremendous success in various domains and are appealing in being able to borrow information by clustering samples that share identical parameters. However, such methods can face hurdles in heterogeneous settings where objects are expected to cluster only along a subset of axes or where clusters of samples share only a subset of identical parameters. We overcome such limitations by developing a novel class of product of Dirichlet process location-scale mixtures that enables independent clustering at multiple scales, which results in varying levels of information sharing across samples. First, we develop the approach for independent multivariate data. Subsequently we generalize it to multivariate time-series data under the framework of multi-subject Vector Autoregressive (VAR) models that is our primary focus, which go beyond parametric single-subject VAR models. We establish posterior consistency and develop efficient posterior computation for implementation. Extensive numerical studies involving VAR models show distinct advantages over competing methods in terms of estimation, clustering, and feature selection accuracy. Our resting state fMRI analysis from the Human Connectome Project reveals biologically interpretable connectivity differences between distinct intelligence groups, while another air pollution application illustrates the superior forecasting accuracy compared to alternate methods.

The non-alcoholic beverage market in the U.S. is a multi-billion dollar industry growing steadily over the past decade. Also, non-alcoholic beverages are among the most heavily advertised food and beverage groups in the United States. Several studies pertaining to non-alcoholic beverages including the incorporation of advertising effects have been conducted, but most of these have centered attention on milk consumption. Some studies have considered demand interrelationships for several beverages including advertising effects in systems-wide analyses. In our analysis, we develop and employ a unique monthly time-series data set derived from Nielsen Homsescan panels for household purchases of non-alcoholic beverages over the period from January 1998 through December 2009. This data set is subjected to the use of vector autoregression (VAR) and polynomial distributed lags (PDL) to examine own-advertising and cross-advertising effects for non-alcoholic beverages. Contemporaneous causal structures among advertising expenditures, quantities and prices of various non-alcoholic beverages also are studied using artificial intelligence approaches such as directed acyclic graphs (DAGs). Once the VAR and PDL (through augmenting the AIDS model) are estimated, we can ascertain own- and cross-advertising effects of various beverages. Impulse response functions gleaned from the use of the VAR approach would show us the impacts of advertising expenditures of various beverages to a one-time-only shock. The direction and the strength of the impulse would speak to the effect of the adverting expenditure of one beverage on others. Error variance decompositions help us determine the magnitude of the contribution of advertising expenditures of several beverages on a given beverage. Similarly, own- and cross-adverting expenditure elasticities generated through the PDL help us determine the negative and positive spillover effects of advertising expenditure of one beverage on several others. The contemporaneous causal structure pertaining to advertising expenditures, quantities and prices of various beverages help identify endogenous, exogenous and/or weakly exogenous factors. Ultimately, the better model to ascertain the effect of advertising expenditures of various non-alcoholic beverages is identified through the study of out-of-sample forecasts generated though both models.

This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.

Structurally regularized learning methods have been attracting attention in the field of statistics, machine learning, and signal processing as useful analysis tools for learning in high-dimensional settings. Using these methods, specific structure can be represented in parameters. In multivariate time series data, it is important to capture time-delay structure where necessary lag length to explain each variable is different. In this paper, we deal with a problem to discover the optimal number of order for each variable in VAR models. When we apply conventional structurally regularized learning methods such as Group Lasso, some problems, which include loss of necessary coefficients, occur. To overcome these problems, we propose a method based on the Latent Group LASSO regularization. To apply it to VAR models, we introduce overlapped coefficient groups in time direction and use a heavy-tailed distribution, such as Student's t-distribution, as a prior to enhance its sparsity. We evaluated the effectiveness of the proposed method using artificial data and actual data set (Beijing PM2.5 data set). As the result, we obtained the sparse models that capture ununiform time-delay structure represented by fewer variables with the same degree of forecast accuracy comparing with ordinary VAR models.

This study analysed the dynamics of the international soybean market using econometric techniques and economic models to study the impacts of the US–China trade war. It considered the analysis of “spatial” (horizontal) price transmission during an approximately ten-year period from September 2009 to May 2019 using monthly time-series data. The research focused on the leaders in the international soybean market, namely, China, the USA, the EU, Brazil and Argentina. Several econometric techniques were employed. The stationarity of the price time series was determined using the augmented Dickey–Fuller (ADF) unit root test. Structural breaks were inferred using the ADF test with a breaks test and a Bai–Perron multiple break test. The long-term relation/cointegration amongst the series was determined using the Johansen cointegration test (1988), with the previous breaks input as dummy variables. The direction of the causality was inferred using the Granger causality test (1969). The long-term and short-term causal relations were determined using the vector autoregression model (VAR) and the vector error correction model (VECM). The results showed a highly efficient and cointegrated market. The incidents of the trade war, as represented by tariffs and subsidies, had minor effects on the market efficacy, cointegration and price transmission. The arbitrage process of the studied market managed to get around the tariffs. In other words, there was no empirical evidence to support the claim that the law of one price (LOOP) did not hold.