Forecasting performance of nonlinear time-series models: an application to weather variable
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ORIGINAL ARTICLE
Forecasting performance of nonlinear time‑series models: an application to weather variable Md. Karimuzzaman1 · Md. Moyazzem Hossain1 Received: 29 January 2020 / Accepted: 21 May 2020 © Springer Nature Switzerland AG 2020
Abstract Modelling the dynamic dependent data by the linear approach is the most popular among the researchers because of its simplicity in calculation and approximation, however, in real-world phenomena, most of the time-dependent data follow the nonlinearity. Moreover, most of the nonlinear modelling of time-dependent data have found in the financial applications. Besides this sector, the authors of this paper found the presence of nonlinearity in meteorological data with the help of four popular nonlinearity tests. Furthermore, there is a scarcity of the application of regime-switching threshold autoregressive nonlinear time-series model in forecasting the weather variables like temperature. Thus, this paper aims to compare the forecasting accuracy of the linear autoregressive (linear AR), self-exciting threshold autoregression (SETAR), logistic smooth transition autoregressive model (LSTAR), and feed-forward neural network (ANNs) and fitted with the determination of regime and hyperparameters. After fitting the models, twenty steps ahead forecast considered for the comparison along with the selected model selection criteria; and results depict that the LSTAR models are selected as the most appropriate fitted models for forecasting the daily Average, Maximum and Minimum temperature. Finally, it has observed that the average, as well as maximum temperature of Dhaka, Bangladesh, have an increasing trend and minimum temperature having a decreasing trend. Keywords Nonlinearity test · Threshold autoregression · Regime switching · Model selection · Forecasting · Temperature · Bangladesh
Introduction Time-series analysis deals with real-world phenomena under the concept of dynamic dependencies. Most of the researchers focus on the linear time-series models because of their long history of successful applications as well as straightforward calculations and good approximation. Moreover, linear processes and models are often adequate in making inferences about the time-series-related phenomena as these models dominate the research from the past decades. Probably the linear autoregressive (Linear AR) model and its branches are the most extensively used time-series model to predict future values with the help of a linear combination of past values. The simple idea of stochastic deference in the * Md. Moyazzem Hossain [email protected] Md. Karimuzzaman [email protected] 1
Department of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh
autoregressive model can deliver accurate forecasts together with a random error in a series. But there is an issue of the biasness in terms of forecasting the high frequent or longterm time series and financial data, e.g. hourly temperature data, four-minute stock price data (Olson and Wu 2020). Conversely, nonlinear
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