Uncertain Autoregressive Model via LASSO Procedure

2020 ◽  
Vol 28 (06) ◽  
pp. 939-956
Author(s):  
Ziqian Zhang ◽  
Xiangfeng Yang ◽  
Jinwu Gao
Keyword(s):  
Time Series ◽  
Autoregressive Model ◽  
Time Series Data ◽  
Series Data ◽  
Temporal Dependence ◽  
Basic Model ◽  
Final Prediction Error ◽  
Optimum Order ◽  
Selection Operator ◽  
Uncertain Time

Uncertain time series analysis is a method to predict future values based on imprecisely observed values. As a basic model of uncertain time series, an uncertain autoregressive model has been presented. However, the existing paper ignores the temporal dependence information embedded in time-series data. In dealing with this issue, this paper adds a least absolute shrinkage and selection operator penalty to the traditional uncertain autoregressive model and selects the optimum order of the model according to Akaike’s final prediction error criterion. Finally, two numerical examples are given to illustrate the effectiveness of the model and compare the results predicted by the uncertain autoregressive model with the principle of least squares.

scholarly journals Time Series Modeling on Monthly Data of Tourist Arrivals in Nepal: An Alternative Approach

2017 ◽  
Vol 1 ◽  
pp. 41-54 ◽  
Author(s):  
Amrit Subedi
Keyword(s):  
Time Series ◽  
Autoregressive Model ◽  
Time Series Data ◽  
Polynomial Function ◽  
Seasonal Fluctuation ◽  
Ar Model ◽  
Series Data ◽  
Monthly Data ◽  
Annual Trend ◽  
Alternative Approach

Background: There are various approaches of modeling on time series data. Most of the studies conducted regarding time series data are based on annual trend whereas very few concerned with data having monthly fluctuation. The data of tourist arrivals is an example of time series data with monthly fluctuation which reveals that there is higher number of tourist arrivals in some months/seasons whereas others have less number. Starting from January, it makes a complete cycle in every 12 months with 3 bends indicating that it can be captured by biquadratic function.Objective: To provide an alternative approach of modeling i.e. combination of Autoregressive model with polynomial (biquadratic) function on time series data with monthly/seasonal fluctuation and compare its adequacy with widely used cyclic autoregressive model i.e. AR (12).Materials and Methods: This study is based on monthly data of tourist arrivals in Nepal. Firstly, usual time series model AR (12) has been adopted and an alternative approach of modeling has been attempted combining AR and biquadratic function. The first part of the model i.e. AR represents annual trend whereas biquadratic part does for monthly fluctuation.Results: The fitted cyclic autoregressive model on monthly data of tourist arrivals is Est. Ym = 3614.33 + 0.9509Ym-12, (R2=0.80); Est. Ym indicates predicted tourist arrivals for mth month and Ym-12 indicates observed tourist arrivals in (m-12)th month and the combined model of AR and biquadratic function is Est. Yt(m) = -46464.6 + 1.000Yt-1 + 52911.56m - 17177m2 + 2043.95m3 - 79.43m4, (R2=0.78); Est. Yt(m) indicates predicted tourist arrivals for mth month of tth year and Yt-1 indicates average tourist arrivals in (t-1)th year. The AR model combined with polynomial function reveals normal and homoscedastic residuals more accurately compared to first one.Conclusion: The use of polynomial function combined with autoregressive model can be useful for time series data having seasonal fluctuation. It can be an alternative approach for picking up a good model for such type of data. Nepalese Journal of Statistics, 2017,  Vol. 1, 41-54

Effective temporal dependence discovery in time series data

2018 ◽  
Vol 11 (8) ◽  
pp. 893-905 ◽  
Author(s):  
Qingchao Cai ◽  
Zhongle Xie ◽  
Meihui Zhang ◽  
Gang Chen ◽  
H. V. Jagadish ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Temporal Dependence

scholarly journals Approaches of Handling Uncertain Time Series Data towards Prediction

2016 ◽  
Vol 5 (6) ◽  
pp. 233-236
Author(s):  
Radzuan M. F. Nabilah ◽  
◽  
Zalinda Othman ◽  
Bakar A. Azuraliza
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Uncertain Time

On the Autoregressive Time Series Model Using Real and Complex Analysis

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 716-728
Author(s):  
Torsten Ullrich
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Closed Form ◽  
Autoregressive Model ◽  
Complex Analysis ◽  
Time Series Data ◽  
Selection Process ◽  
Global Structure ◽  
Series Data ◽  
Time Step

The autoregressive model is a tool used in time series analysis to describe and model time series data. Its main structure is a linear equation using the previous values to compute the next time step; i.e., the short time relationship is the core component of the autoregressive model. Therefore, short-term effects can be modeled in an easy way, but the global structure of the model is not obvious. However, this global structure is a crucial aid in the model selection process in data analysis. If the global properties are not reflected in the data, a corresponding model is not compatible. This helpful knowledge avoids unsuccessful modeling attempts. This article analyzes the global structure of the autoregressive model through the derivation of a closed form. In detail, the closed form of an autoregressive model consists of the basis functions of a fundamental system of an ordinary differential equation with constant coefficients; i.e., it consists of a combination of polynomial factors with sinusoidal, cosinusoidal, and exponential functions. This new insight supports the model selection process.

scholarly journals Chebyshev Similarity Match between Uncertain Time Series

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Wang ◽  
Guohua Liu ◽  
Dingjia Liu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Computational Cost ◽  
Random Variable ◽  
Repeated Measurements ◽  
Series Data ◽  
Chebyshev Inequality ◽  
Central Tendency ◽  
Uncertain Time ◽  
Estimation Interval

In real application scenarios, the inherent impreciseness of sensor readings, the intentional perturbation of privacy-preserving transformations, and error-prone mining algorithms cause much uncertainty of time series data. The uncertainty brings serious challenges for the similarity measurement of time series. In this paper, we first propose a model of uncertain time series inspired by Chebyshev inequality. It estimates possible sample value range and central tendency range in terms of sample estimation interval and central tendency estimation interval, respectively, at each time slot. In comparison with traditional models adopting repeated measurements and random variable, Chebyshev model reduces overall computational cost and requires no prior knowledge. We convert Chebyshev uncertain time series into certain time series matrix; therefore noise reduction and dimensionality reduction are available for uncertain time series. Secondly, we propose a new similarity matching method based on Chebyshev model. It depends on overlaps between two sample estimation intervals and overlaps between central tendency estimation intervals from different uncertain time series. At the end of this paper, we conduct an extensive experiment and analyze the results by comparing with prior works.

scholarly journals Soft Computation Vector Autoregressive Neural Network (VAR-NN) GUI-Based

2018 ◽  
Vol 73 ◽  
pp. 13008 ◽  
Author(s):  
Hasbi Yasin ◽  
Budi Warsito ◽  
Rukun Santoso ◽  
Suparti
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Autoregressive Model ◽  
Stock Price ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Vector Autoregressive ◽  
Price Data

Vector autoregressive model proposed for multivariate time series data. Neural Network, including Feed Forward Neural Network (FFNN), is the powerful tool for the nonlinear model. In autoregressive model, the input layer is the past values of the same series up to certain lag and the output layers is the current value. So, VAR-NN is proposed to predict the multivariate time series data using nonlinear approach. The optimal lag time in VAR are used as aid of selecting the input in VAR-NN. In this study we develop the soft computation tools of VAR-NN based on Graphical User Interface. In each number of neurons in hidden layer, the looping process is performed several times in order to get the best result. The best one is chosen by the least of Mean Absolute Percentage Error (MAPE) criteria. In this study, the model is applied in the two series of stock price data from Indonesia Stock Exchange. Evaluation of VAR-NN performance was based on train-validation and test-validation sample approach. Based on the empirical stock price data it can be concluded that VAR-NN yields perfect performance both in in-sample and in out-sample for non-linear function approximation. This is indicated by the MAPE value that is less than 1% .

Modelling the supply of new residential construction in Aberdeen, UK

2014 ◽  
Vol 7 (3) ◽  
pp. 346-362 ◽  
Author(s):  
Anthony Owusu-Ansah
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
House Prices ◽  
Local Level ◽  
Series Data ◽  
Local Markets ◽  
Content Type ◽  
Time On The Market ◽  
Basic Model ◽  
Housing Starts

Purpose – The purpose of this paper is to use local-level time series data to examine the determinants of housing starts and the price elasticity of supply for the Aberdeen local housing market. Design/methodology/approach – Seven time series models are used in the analysis. The basic model treats housing starts as a function of the changes of current and lagged house prices, interest rate and construction cost. The other six models which are extensions of the basic model include other variables like time on the market, planning constraints and future expectations. Findings – It is found that the local variables – changes in house prices, time on the market, planning regulation, lagged stock and lagged and future housing starts – are the main factors that influence new residential construction in Aberdeen. None of the national variables is significant, confirming the importance of limiting housing market analysis to the local level. The price elasticity of supply estimated is in the range of 2.0 to 3.2 for housing starts and 0.01 to 0.02 for housing stock. These estimates are higher than most of the elasticities for the other UK local markets. Originality/value – There is the need to better understand the supply of housing at the various local housing markets. Unfortunately, however, most housing supply studies use national data. Because national data are aggregation of local data, using national studies results for local markets may be uninformative. Also, the few existing local studies use typically cross-section data or at least time series over relatively short time spans. This paper makes an effort to use quarterly time series data over a 25-year period for a local market and also include a planning variable which is different from local markets and often ignored in national or regional studies.

Use of Vector Autoregressive Model for Anomaly Detection in Utility Gas Turbines

2019 ◽  
Author(s):  
Vipul Goyal ◽  
Mengyu Xu ◽  
Jayanta Kapat
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Autoregressive Model ◽  
Gas Turbines ◽  
Time Series Data ◽  
Combined Cycle ◽  
Series Data ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
Multi Stage

Abstract This study is based on time-series data from the combined cycle utility gas turbines consisting of three-gas turbine units and one steam turbine unit. We construct a multi-stage vector autoregressive model for the nominal operation of powerplant assuming sparsity in the association among variables and use this as a basis for anomaly detection and prediction. This prediction is compared with the time-series data of the plant-operation containing anomalies. Granger causality networks, which are based on the associations between the time series streams, are learned as an important implication from the vector autoregressive modelling. Anomaly is detected by comparing the observed measurements against their predicted value.

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