A Novel Time Series Forecasting Approach Considering Data Characteristics

A Novel Time Series Forecasting Approach Considering Data Characteristics

Ling Tang, Shuai Wang, Lean Yu
Copyright: © 2014 |Pages: 8
DOI: 10.4018/ijkss.2014070104
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Abstract

A novel time series forecasting approach with consideration of inner knowledge hidden in data, in terms of data characteristics, is proposed. In the proposed methodology, the main data characteristics hidden in the observed time series data are first explored; and according to the data characteristics, suitable forecasting models are formulated to improve prediction performance. For illustration, the proposed methodology is used to predict Chinese total social consumption and total energy consumption. The empirical results show the forecasting model considering data characteristics outperforms other popular forecasting models ignoring data characteristics, which further implies that data characteristics exploration is an important and necessary step in forecasting and the proposed methodology can be used as a promising approach for time series forecasting.
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2. Methodology

Two main steps are involved in the novel data-characteristic based forecasting approach: data characteristics testing and forecasting model formulation. In the first step, the main data characteristics of the observed data are thoroughly tested. The second step is to formulate an appropriate forecasting model, in terms of the data characteristics. The general framework can be designed, as illustrated in Table 1.

Table 1.
General framework of data-characteristic based time series forecasting approach
Step 1: Data Characteristics TestingStep 2: Forecasting Model Formulation
StationarityLinearitySeasonalityTypeSample
√√×Traditional ToolsARIMA
√√√Traditional Tools with
Seasonal Dummies		SARIMA
×××AI toolsANN
××√AI tools with Seasonal
Dummies		Seasonal ANN

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