Application of wavelet decomposition in time-series forecasting

Zhang, Keyi; Gençay, Ramazan; Yazgan, M. Ege

doi:10.1016/j.econlet.2017.06.010

Cited by 58 publications

(27 citation statements)

References 11 publications

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“…Our results also explain that wavelet-based time-frequency analysis offers more explanation regarding the effects of (il)iquidity on EPU across different time horizons or investment scales. This is consistent with Zhang, Gençay, and Yazgan (2017) explanation that time series decomposition can improve the efficiency of forecasts of original series. Second, our findings in terms of the EPU and (il)liquidity relationship during crisis periods provide further insights into policy implications.…”

Section: Introductionsupporting

confidence: 91%

Economic policy uncertainty and stock market liquidity: Evidence from G7 countries

et al. 2019

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In the aftermath of the 2007-2008 global financial crisis, there were heightened concerns among the market participants and policymakers on the potential adverse effects of economic policies on stock market liquidity. This paper examines the causality and co-movement between economic policy uncertainties and stock market liquidity using monthly data from G7 countries. Our empirical analysis considers wavelet coherence and wavelet phase angle tests. Linear and nonlinear causality test results suggest that a causal relationship exists between economic policy uncertainty and stock market liquidity. Moreover, stock market illiquidity varies with the uncertainty but in the same direction while liquidity co-moves in the opposite direction. In times of economic turmoil or crises, the relationship between policy uncertainty and illiquidity becomes stronger, and illiquidity leads economic policy uncertainty. Overall, our findings indicate that the leading indicator property of (il)liquidity is useful for providing economic information and thereby to manage market conditions and investor expectations.

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Section: Introductionsupporting

confidence: 91%

Economic policy uncertainty and stock market liquidity: Evidence from G7 countries

et al. 2019

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“…In recent years, there are some time series prediction works using time series decomposition in several search areas. A regression model combined with wavelet transform is proposed to forecast the future value of the S&P 500 [11]. EMD is used for electricity load forecasting [12].…”

Section: Decomposition Methodsmentioning

confidence: 99%

Prediction of HFMD Cases by Leveraging Time Series Decomposition and Local Fusion

Wang

Lin

et al. 2021

Wireless Communications and Mobile Computing

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Hand, foot, and mouth disease (HFMD) is an infection that is common in children under 5 years old. This disease is not a serious disease commonly, but it is one of the most widespread infectious diseases which can still be fatal. HFMD still poses a threat to the lives and health of children and adolescents. An effective prediction model would be very helpful to HFMD control and prevention. Several methods have been proposed to predict HFMD outpatient cases. These methods tend to utilize the connection between cases and exogenous data, but exogenous data is not always available. In this paper, a novel method combined time series composition and local fusion has been proposed. The Empirical Mode Decomposition (EMD) method is used to decompose HFMD outpatient time series. Linear local predictors are applied to processing input data. The predicted value is generated via fusing the output of local predictors. The evaluation of the proposed model is carried on a real dataset comparing with the state-of-the-art methods. The results show that our model is more accurately compared with other baseline models. Thus, the model we proposed can be an effective method in the HFMD outpatient prediction mission.

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“…Our approach builds on the literature that uses discrete wavelet methods to forecast out-of-sample economic and nancial time series. Examples include Rua (2011Rua ( , 2017, who forecast GDP growth and ination using a factor-augmented wavelets approach; Zhang, Gençay, and Yazgan (2017) and Faria and Verona (2017, 2018, 2020b, who focus on forecasting stock market returns; Caraiani 3 Details on our data and on our econometric approach are presented in section 3. Specically, the HF component is the sum of frequencies D 1 and D 2 , the BCF component is the sum of frequencies D 3 and D 4 , and the LF component is the sum of frequencies D 5 and D 6 .…”

Section: Frequency-domain Forecastsmentioning

confidence: 99%

Forecasting Inflation with the New Keynesian Phillips Curve: Frequency Matters

Martins

Verona

2020

SSRN Journal

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We show that the New Keynesian Phillips Curve (NKPC) outperforms standard benchmarks in forecasting U.S. ination once frequency-domain information is taken into account.We do so by decomposing the time series (of ination and its predictors) into several frequency bands and forecasting separately each frequency component of ination. The largest statistically signicant forecasting gains are achieved with a model that forecasts the lowest frequency component of ination (corresponding to cycles longer than 16 years) exibly using information from all frequency components of the NKPC ination predictors. Its performance is particularly good in the returning to recovery from the Great Recession.

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Application of wavelet decomposition in time-series forecasting

Cited by 58 publications

References 11 publications

Economic policy uncertainty and stock market liquidity: Evidence from G7 countries

Economic policy uncertainty and stock market liquidity: Evidence from G7 countries

Prediction of HFMD Cases by Leveraging Time Series Decomposition and Local Fusion

Forecasting Inflation with the New Keynesian Phillips Curve: Frequency Matters

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