nep-for New Economics Papers
on Forecasting
Issue of 2023‒10‒16
two papers chosen by
Rob J Hyndman, Monash University
  1. Electricity Consumption Forecasting in Algeria using ARIMA and Long Short-Term Memory Neural Network By Sahed Abdelkader; Kahoui Hacene
  2. Combining Forecasts under Structural Breaks Using Graphical LASSO By Tae-Hwy Lee; Ekaterina Seregina
  1. By: Sahed Abdelkader (Maghnia University Center); Kahoui Hacene (maghnia University center, Algeria)
    Abstract: Forecasting electricity consumption is necessary for electric grid operation and utility resource planning, as well as to improve energy security and grid resilience. Thus, this research aims to investigate the prediction performance of the ARIMA and LSTM neural network model using electricity consumption data during the period 1990 to 2020. The time series for electricity consumption is divided into 70% for training data and 30% for test data. The results showed that the LSTM model provided improved forecasting accuracy than the ARIMA model.
    Keywords: Electricity Consumption ARIMA LSTM Algeria. JEL Classification Codes: Q47, C53, C45, Electricity Consumption, ARIMA, LSTM, Algeria. JEL Classification Codes: Q47
    Date: 2023–06–04
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-04183403&r=for
  2. By: Tae-Hwy Lee (Department of Economics, University of California Riverside); Ekaterina Seregina (Colby College)
    Abstract: In this paper we develop a novel method of combining many forecasts based on a machine learning algorithm called Graphical LASSO (GL). We visualize forecast errors from different forecasters as a network of interacting entities and generalize network inference in the presence of common factor structure and structural breaks. First, we note that forecasters often use common information and hence make common mistakes,  which makes the forecast errors exhibit common factor structures. We use the Factor Graphical LASSO (FGL, Lee and Seregina (2023)) to separate common forecast errors from the idiosyncratic errors and exploit sparsity of the precision matrix of the latter. Second, since the network of experts changes over time as a response to unstable environments such as recessions, it is unreasonable to assume constant forecast combination weights. Hence, we propose Regime-Dependent Factor Graphical LASSO (RD-FGL) that allows factor loadings and idiosyncratic precision matrix to be regime-dependent. We develop its scalable implementation using the Alternating Direction Method of Multipliers (ADMM) to estimate regime-dependent forecast combination weights. The empirical application to forecasting macroeconomic series using the data of the European Central Bank’s Survey of Professional Forecasters (ECB SPF) demonstrates superior performance of a combined forecast using FGL and RD-FGL.
    Keywords: Common Forecast Errors, Regime Dependent Forecast Combination, Sparse Precision Matrix of Idiosyncratic Errors, Structural Breaks.
    JEL: C13 C38 C55
    Date: 2023–09
    URL: http://d.repec.org/n?u=RePEc:ucr:wpaper:202310&r=for
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