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on Forecasting |
| By: | Aryan Bhambu; Arabin Kumar Dey |
| Abstract: | This research paper introduces innovative approaches for multivariate time series forecasting based on different variations of the combined regression strategy. We use specific data preprocessing techniques which makes a radical change in the behaviour of prediction. We compare the performance of the model based on two types of hyper-parameter tuning Bayesian optimisation (BO) and Usual Grid search. Our proposed methodologies outperform all state-of-the-art comparative models. We illustrate the methodologies through eight time series datasets from three categories: cryptocurrency, stock index, and short-term load forecasting. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.04539&r= |
| By: | Monika Zimmermann; Florian Ziel |
| Abstract: | Accurate mid-term (weeks to one year) hourly electricity load forecasts are essential for strategic decision-making in power plant operation, ensuring supply security and grid stability, and energy trading. While numerous models effectively predict short-term (hours to a few days) hourly load, mid-term forecasting solutions remain scarce. In mid-term load forecasting, besides daily, weekly, and annual seasonal and autoregressive effects, capturing weather and holiday effects, as well as socio-economic non-stationarities in the data, poses significant modeling challenges. To address these challenges, we propose a novel forecasting method using Generalized Additive Models (GAMs) built from interpretable P-splines and enhanced with autoregressive post-processing. This model uses smoothed temperatures, Error-Trend-Seasonal (ETS) modeled non-stationary states, a nuanced representation of holiday effects with weekday variations, and seasonal information as input. The proposed model is evaluated on load data from 24 European countries. This analysis demonstrates that the model not only has significantly enhanced forecasting accuracy compared to state-of-the-art methods but also offers valuable insights into the influence of individual components on predicted load, given its full interpretability. Achieving performance akin to day-ahead TSO forecasts in fast computation times of a few seconds for several years of hourly data underscores the model's potential for practical application in the power system industry. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.17070&r= |
| By: | Paul Ghelasi; Florian Ziel |
| Abstract: | The surge in global energy prices during the recent energy crisis, which peaked in 2022, has intensified the need for mid-term to long-term forecasting for hedging and valuation purposes. This study analyzes the statistical predictability of power prices before, during, and after the energy crisis, using econometric models with an hourly resolution. To stabilize the model estimates, we define fundamentally derived coefficient bounds. We provide an in-depth analysis of the unit root behavior of the power price series, showing that the long-term stochastic trend is explained by the prices of commodities used as fuels for power generation: gas, coal, oil, and emission allowances (EUA). However, as the forecasting horizon increases, spurious effects become extremely relevant, leading to highly significant but economically meaningless results. To mitigate these spurious effects, we propose the "current" model: estimating the current same-day relationship between power prices and their regressors and projecting this relationship into the future. This flexible and interpretable method is applied to hourly German day-ahead power prices for forecasting horizons up to one year ahead, utilizing a combination of regularized regression methods and generalized additive models. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.00326&r= |
| By: | Massimiliano MARCELLINO; Michael PFARRHOFER |
| Abstract: | We review specification and estimation of multivariate Bayesian nonparametric models for forecasting (possibly large sets of) macroeconomic and financial variables. The focus is on Bayesian Additive Regression Trees and Gaussian Processes. We then apply various versions of these models for point, density and tail forecasting using datasets for the euro area and the US. The performance is compared with that of several variants of Bayesian VARs to assess the relevance of accounting for general forms of nonlinearities. We find that medium-scale linear VARs with stochastic volatility are tough benchmarks to beat. Some gains in predictive accuracy arise for nonparametric approaches, most notably for short-run forecasts of unemployment and longer-run predictions of inflation, and during recessionary or otherwise non-standard economic episodes |
| Keywords: | United States, euro area, Bayesian Additive Regression Trees, Gaussian Processes, multivariate time series analysis, structural breaks |
| JEL: | C11 C32 C53 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp24224&r= |
| By: | Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample pe- riod from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability, but hardly evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregres- sive (HAR) RV model. We also study an extended model that features the RVs of energy commodities and precious metals. |
| Keywords: | Agricultural commodities, Realized volatility, Multi-task forecasting |
| JEL: | C22 C32 C53 Q11 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202423&r= |
| By: | Jinyuan Chang; Qin Fang; Xinghao Qiao; Qiwei Yao |
| Abstract: | We propose a two-step procedure to model and predict high-dimensional functional time series, where the number of function-valued time series $p$ is large in relation to the length of time series $n$. Our first step performs an eigenanalysis of a positive definite matrix, which leads to a one-to-one linear transformation for the original high-dimensional functional time series, and the transformed curve series can be segmented into several groups such that any two subseries from any two different groups are uncorrelated both contemporaneously and serially. Consequently in our second step those groups are handled separately without the information loss on the overall linear dynamic structure. The second step is devoted to establishing a finite-dimensional dynamical structure for all the transformed functional time series within each group. Furthermore the finite-dimensional structure is represented by that of a vector time series. Modelling and forecasting for the original high-dimensional functional time series are realized via those for the vector time series in all the groups. We investigate the theoretical properties of our proposed methods, and illustrate the finite-sample performance through both extensive simulation and two real datasets. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.00700&r= |