A fuzzy seasonal ARIMA model for forecasting

Tseng, Fang-Mei; Tzeng, Gwo‐Hshiung

doi:10.1016/s0165-0114(01)00047-1

Cited by 156 publications

(70 citation statements)

References 12 publications

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“…The autocorrelation (ACF) and partial autocorrelation (PACF) graphs are obtained through reviews. After observation, the paper finds that autocorrelation coefficient and partial autocorrelation coefficient [7] are near 0. So there is no truncation and smearing.…”

Section: Model Establishmentmentioning

confidence: 95%

Analysis of CSI 300 Stock Index Futures Price Trend Based on ARIMA Model

Zhang¹,

Sun²

2018

dtssehs

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Abstract. Stock index futures has become the major tools for investors to avoid risk or make speculative arbitrage. However, the leverage of stock index futures is large. If they cannot make a correct judgment on the trend of stock index futures, the loss will be great. This paper is based on this background and engages in intensive study. Using traditional and modern econometric methods respectively, the CSI 300 index futures were fitted. In addition, RMSE (root mean square error) was used to find the optimal model. After finding the model, we can put the trading strategy forward.

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Section: Model Establishmentmentioning

confidence: 95%

Analysis of CSI 300 Stock Index Futures Price Trend Based on ARIMA Model

Zhang¹,

Sun²

2018

dtssehs

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“…Before starting the training process, the data (actual prices) are normalized between 0 and 1 using the min-max formula using eq. (42). In this case five lagged scaled prices are used as inputs for current day price prediction i.e.…”

Section: Dataset-2mentioning

confidence: 99%

“…In recent years, more hybrid forecasting models have been proposed using feedforward neural networks and applied in many areas with good prediction performance. Among the various hybrid models, like the Fuzzy information systems with ARIMA [42][43], Fuzzy wavelet neural networks with GARCH [40], etc. have been successfully applied in time series prediction.…”

Section: Introductionmentioning

confidence: 99%

Times Series Forecasting using Chebyshev Functions based Locally Recurrent neuro-Fuzzy Information System

Parida¹,

Bisoi²,

Dash³

et al. 2017

IJCIS

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The model proposed in this paper, is a hybridization of fuzzy neural network (FNN) and a functional link neural system for time series data prediction. The TSK-type feedforward fuzzy neural network does not take the full advantage of the use of the fuzzy rule base in accurate input-output mapping and hence a hybrid model is developed using the Chebyshev polynomial functions to construct the consequent part of the fuzzy rules. The model to be known as locally recurrent neuro fuzzy information system (LRNFIS) is used to provide an expanded nonlinear transformation to the input space thereby increasing its dimension which will be adequate to capture the nonlinearities and chaotic variations in the time series. The locally recurrent nodes will provide feedback connections between outputs and inputs allowing signal flow in both forward and backward directions, giving the network a dynamic memory useful to mimic dynamic systems. For training the proposed LRNFIS, an improved firefly-harmony search (IFFHS) learning algorithm is used to estimate the parameters of the consequent part and feedback loop parameters. Three real world time series databases like the electricity price of PJM electricity market, the widely studied currency exchange rates between US Dollar (USD) and other four currencies i.e. Australian Dollar (AUD), Swiss Franc (CHF), Mexican Peso (MXN), Brazilian Real (BRL), along with S&P 500 and Nikkei 225 stock market data are used for performance validation of the newly proposed LRNFIS.

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“…ARIMA is originally proposed by Box and Jenkins [31], which is a very popular model for time series analysis and forecasting [32], [33]. It collects past values of the same variable and develops a model to describe their underlying relationships.…”

Section: Arima Modelsmentioning

confidence: 99%

Personalized Qos Prediction Using Time Series Forecasting and Collaborative Filtering

Hu¹,

Peng²,

Hu³

et al. 2016

STSC

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Quality of Service (QoS) has been widely used for web service recommendation and selection. Since QoS information usually cannot be predetermined, how to make personalized QoS prediction precisely becomes a big challenge. Time series forecasting and Collaborative Filtering (CF) are two mainstream technologies for QoS prediction. However, existing time series forecasting approaches based on AutoRegressive Integrated Moving Average (ARIMA) models do not take new observations as a feedback to revise QoS forecasts. Moreover, they only focus on forecasting QoS values for each individual web service. Service users' personalized factors are not considered. CF facilitates personalized QoS evaluation, but rarely models the temporal dynamics of QoS values. To address the limitations of existing approaches, this paper proposes a novel personalized QoS prediction approach considering both the temporal dynamics of QoS attributes and the personalized factors of service users. Our approach seamlessly combines CF with an improved time series forecasting method, which uses Kalman filtering to compensate for defects of ARIMA models. Additionally, we design a prototype system for QoS dissemination over the Internet, thus providing a necessary infrastructure for the implementation of personalized QoS prediction. Finally, we conduct experiments to study the effectiveness of our approach. Experimental results show our approach can improve QoS prediction accuracy significantly.

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A fuzzy seasonal ARIMA model for forecasting

Cited by 156 publications

References 12 publications

Analysis of CSI 300 Stock Index Futures Price Trend Based on ARIMA Model

Analysis of CSI 300 Stock Index Futures Price Trend Based on ARIMA Model

Times Series Forecasting using Chebyshev Functions based Locally Recurrent neuro-Fuzzy Information System

Personalized Qos Prediction Using Time Series Forecasting and Collaborative Filtering

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