One-step Prediction of Financial Time Series

Ramaswamy, Srichander

doi:10.2139/ssrn.856985

Cited by 2 publications

(1 citation statement)

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“…Recently, a learning technique based on Weighted Nearest Neighbors (WNN) has been applied to forecast the next day hourly energy consumption and hourly energy price, reporting promising results . The nearest neighbor method is capable of accounting for both the nonlinearities and the nonstationarity of the given time‐series data , and WNN can be used to find and weight similar load data to predict the day ahead load. The WNN technique is dependent on two critical parameters, namely the embedding dimension and the optimum number of nearest neighbors for forecast accuracy.…”

Section: Introductionmentioning

confidence: 99%

A wavelet‐nearest neighbor model for short‐term load forecasting

Sudheer

Suseelatha

2014

Energy Science & Engineering

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Load forecasts of short lead times ranging from an hour to a day ahead are essential for improving the economic efficiency and reliability of power systems. This paper proposes a hybrid model based on the wavelet transform (WT) and the weighted nearest neighbor (WNN) techniques to predict the day ahead electrical load. The WT is used to decompose the load series into deterministic series and fluctuation series that reflect the changing dynamics of data. The two subseries are then separately forecast using appropriately fitted WNN models. The final forecast is obtained by composing the predicted results of each subseries. The hourly electrical load of California and Spanish energy markets are taken as experimental data and the mean absolute percentage error (MAPE), Weekly MAPE (WMAPE) and Monthly MAPE (MMAPE) are computed to evaluate the forecasting performance of the next-day load forecasts. The forecasting efficiency of the proposed model is evaluated using db2, db4, db5 and bior 3.1 wavelets. The results demonstrate the forecasting accuracy of the proposed hybrid model.

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Section: Introductionmentioning

confidence: 99%