Fast Forecast Reconciliation Using Linear Models

نویسندگان

چکیده

Forecasting hierarchical or grouped time series usually involves two steps: computing base forecasts and reconciling the forecasts. Base can be computed by popular forecasting methods such as Exponential Smoothing (ETS) Autoregressive Integrated Moving Average (ARIMA) models. The reconciliation step is a linear process that adjusts to ensure they are coherent. However using ETS ARIMA for computationally challenging when there large number of forecast, each model must numerically optimized series. We propose avoids this computational problem handles in single step. proposed method very flexible incorporating external data, handling missing values selection. illustrate our approach datasets: monthly Australian domestic tourism daily Wikipedia pageviews. compare ARIMA, show much faster while providing similar levels forecast accuracy.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Using Sub-models for Dynamic Data Reconciliation

The optimal approach for dynamic data reconciliation consists in using a complete and exact process model. Unfortunately, such a model is difficult to obtain in industrial practice. Through an example, several observers based on static, stationary and dynamic sub-models are designed and compared to the optimal approach. The comparisons illustrate that, for the given conditions, static observers...

متن کامل

Parallelizing Operational Weather Forecast Models for Portable and Fast Execution

This paper describes a high level library (The Nearest Neighbor Tool: NNT) that has been used to parallelize operational weather prediction models. NNT is part of the Scalable Modeling System (SMS), developed at the Forecast Systems Laboratory (FSL). Programs written in NNT rely on SMS's run-time system and port between a wide range of computing platforms, performing well in multiprocessor syst...

متن کامل

Combining Var Forecast Densities Using Fast Fourier Transform

In this paper, I propose the use of fast Fourier transform (FFT) as a convenienttool for combining forecast densities of vector autoregressive models in a hybrid Bayesianmanner. While a vast amount of papers comprises combinations based on normal approxi-mations, Monte Carlo methods were fully utilized here, which made the analysis computa-tionally demanding. For the sake of min...

متن کامل

Forecast generation model of municipal solid waste using multiple linear regression

The objective of this study was to develop a forecast model to determine the rate of generation of municipal solid waste in the municipalities of the Cuenca del Cañón del Sumidero, Chiapas, Mexico. Multiple linear regression was used with social and demographic explanatory variables. The compiled database consisted of 9 variables with 118 specific data per variable, which were analyzed using a ...

متن کامل

Kp forecast models

[1] Magnetically active times, e.g., Kp > 5, are notoriously difficult to predict, precisely the times when such predictions are crucial to the space weather users. Taking advantage of the routinely available solar wind measurements at Langrangian point (L1) and nowcast Kps, Kp forecast models based on neural networks were developed with the focus on improving the forecast for active times. To ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of Computational and Graphical Statistics

سال: 2021

ISSN: ['1061-8600', '1537-2715']

DOI: https://doi.org/10.1080/10618600.2021.1939038