Application of wavelet decomposition in time-series forecasting. (English) Zbl 1396.62221
Summary: Observed time series data can exhibit different components, such as trends, seasonality, and jumps, which are characterized by different coefficients in their respective data generating processes. Therefore, fitting a given time series model to aggregated data can be time consuming and may lead to a loss of forecasting accuracy. In this paper, coefficients for variable components in estimations are generated based on wavelet-based multiresolution analyses. Thus, the accuracy of forecasts based on aggregate data should be improved because the constraint of equality among the model coefficients for all data components is relaxed.
MSC:
| 62M20 | Inference from stochastic processes and prediction |
| 62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |
| 42C40 | Nontrigonometric harmonic analysis involving wavelets and other special systems |
Keywords:
wavelet decomposition; combining forecasts; reconciling forecasts; hierarchical time seriesReferences:
| [1] | Chen, Yu-chin; Turnovsky, Stephen J.; Zivot, Eric, Forecasting inflation using commodity price aggregates, J. Econometrics, 183, 1, 117-134, (2014) · Zbl 1312.91069 |
| [2] | Claeskens, Gerda; Magnus, Jan R.; Vasnev, Andrey L.; Wang, Wendun, The forecast combination puzzle: A simple theoretical explanation, Int. J. Forecast., 32, 3, 754-762, (2016) |
| [3] | Conejo, A. J.; Plazas, M. A.; Espinola, R.; Molina, A. B., Day-ahead electricity price forecasting using the wavelet transform and ARIMA models, IEEE Trans. Power Syst., 20, 2, 1035-1042, (2005) |
| [4] | Del Negro, Marco; Hasegawa, Raiden B.; Schorfheide, Frank, Dynamic prediction pools: an investigation of financial frictions and forecasting performance, J. Econometrics, 391-405, (2016) · Zbl 1420.62408 |
| [5] | Fliedner, Gene, An investigation of aggregate variable time series forecast strategies with specific subaggregate time series statistical correlation, Comput. Oper. Res., 26, 10, 1133-1149, (1999) · Zbl 0940.90004 |
| [6] | Gençay, Ramazan; Selçuk, Faruk; Whitcher, Brandon J., An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, (2001), Academic press · Zbl 1068.42029 |
| [7] | Hyndman, Rob J.; Ahmed, Roman A.; Athanasopoulos, George; Shang, Han Lin, Optimal combination forecasts for hierarchical time series, Comput. Statist. Data Anal., 55, 9, 2579-2589, (2011) · Zbl 1464.62095 |
| [8] | Hyndman, Rob; Khandakar, Yeasmin, Automatic time series forecasting: the forecast package for R, J. Stat. Softw., 27, 1, 1-22, (2008) |
| [9] | Hyndman, Rob J.; Lee, Alan J.; Wang, Earo, Fast computation of reconciled forecasts for hierarchical and grouped time series, Comput. Statist. Data Anal., 97, 16-32, (2016) · Zbl 1468.62086 |
| [10] | Kohn, Robert, When is an aggregate of a time series efficiently forecast by its past?, J. Econometrics, 18, 3, 337-349, (1982) · Zbl 0487.62083 |
| [11] | Rapach, David E.; Strauss, Jack K.; Zhou, Guofu, Out-of-sample equity premium prediction: combination forecasts and links to the real economy, Rev. Financ. Stud., 23, 2, 821-862, (2010) |
| [12] | Tiao, G. C.; Guttman, Irwin, Forecasting contemporal aggregates of multiple time series, J. Econometrics, 12, 2, 219-230, (1980) · Zbl 0435.62099 |
| [13] | Zellner, Arnold; Tobias, Justin, A note on aggregation, disaggregation and forecasting performance, J. Forecast., 19, 5, 457-465, (2000) |
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