Applying Hybrid time series models for modeling bivariate time series data with different distributions for forecasting unemployment rate in the USA

The measurement error problem that we consider in this paper is concerned with the situation where time series data of various kinds—short memory, long memory, and random walk processes—are contaminated by white noise. We suggest a unified approach to testing for the existence of such noise. It is found that the power of our test crucially depends on the underlying process.

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An Analysis on the Unemployment Rate in the Philippines: A Time Series Data Approach

Journal of Physics Conference Series ◽

10.1088/1742-6596/820/1/012008 ◽

2017 ◽

Vol 820 ◽

pp. 012008

Author(s):

J D Urrutia ◽

R L Tampis ◽

JB E Atienza

Keyword(s):

Time Series ◽

Unemployment Rate ◽

Time Series Data ◽

The Philippines ◽

Series Data

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Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18168660 ◽

2021 ◽

Vol 18 (16) ◽

pp. 8660

Author(s):

Isra Al-Turaiki ◽

Fahad Almutlaq ◽

Hend Alrasheed ◽

Norah Alballa

Keyword(s):

Time Series ◽

Saudi Arabia ◽

Time Series Data ◽

Arima Model ◽

Empirical Evaluation ◽

Cubic Spline ◽

Exponential Smoothing ◽

Series Data ◽

Time Series Models ◽

Forecasting Models

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

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Model Rantai Markov dan Model ARIMA serta Kombinasinya dalam Memprediksi Curah Hujan di Kota Makassar

Journal of Mathematics, Computations, and Statistics ◽

10.35580/jmathcos.v1i1.9169 ◽

2019 ◽

Vol 1 (1) ◽

pp. 8

Author(s):

Ahmad Zaki ◽

Wahidah Sanusi ◽

Saiful Bahri

Keyword(s):

Time Series ◽

Markov Chain ◽

Time Series Data ◽

Time Sequence ◽

Monthly Rainfall ◽

Series Data ◽

Time Series Models ◽

Time Series Modeling ◽

Discrete Variable ◽

Future Events

Abstrak. Curah hujan merupakan suatu data deret waktu yang bersifat kontinu, namun juga dapat diformulasikan sebagai peubah diskrit yaitu dengan menggolongkan suatu hari menjadi hujan dan tidak hujan. Curah hujan yang dicatat oleh pos hujan dapat digunakan untuk memprediksi curah hujan pada waktu yang akan datang melalui pemodelan deret waktu ARIMA musiman, Rantai Markov atau dengan campuran keduanya. Proses Markov merupakan suatu sistem stokastik di mana kejadian di masa yang akan datang bergantung pada kejadian sesaat sebelumnya Deret waktu merupakan serangkaian data yang disusun menurut urutan waktu Tujuan penelitian ini adalah untuk memodelkan dan memprediksi curah hujan dengan campuran Rantai Markov dan model deret waktu. Data yang digunakan dalam penelitian ini adalah curah hujan bulanan kota Makassar tahun 2007 sampai 2017. Campuran model deret waktu lebih sesuai digunakan untuk memprediksi curah hujan bulanan dibandingkan dengan pemodelan deret waktu saja hal ini dapat dilihat dai nilai MSE.Kata Kunci: Rantai Markov, Deret Waktu, ARIMA MusimanAbstract. Rainfall is a time series data that is continuous, but can also be formulated as a discrete variable that is by classifying one day as rainy and not rainy. Rainfall recorded by rain posts can be used to predict rainfall in the future through seasonal ARIMA time series modeling, Markov Chain or with a mixture of both. The Markov process is a stochastic system in which future events depend on the events of the previous moment. The time series is a series of data arranged in time sequence. The purpose of this study is to model and predict rainfall with a mixture of Markov Chains and time series models. The data used in this study is the monthly rainfall of Makassar city in 2007 to 2017. A mixture of time series models is more suitable to be used to predict monthly rainfall compared to modeling time series. This can be seen from the MSE value.Keywords: Markov chain, Time Series, seasonal ARIMA.

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Stochastic Claims Reserving Methods with State Space Representations: A Review

Risks ◽

10.3390/risks9110198 ◽

2021 ◽

Vol 9 (11) ◽

pp. 198

Author(s):

Nataliya Chukhrova ◽

Arne Johannssen

Keyword(s):

Time Series ◽

State Space ◽

Life Insurance ◽

Time Series Data ◽

Temporal Dynamics ◽

Insurance Companies ◽

Series Data ◽

Time Series Models ◽

Life Insurer ◽

High Flexibility

Often, the claims reserves exceed the available equity of non-life insurance companies and a change in the claims reserves by a small percentage has a large impact on the annual accounts. Therefore, it is of vital importance for any non-life insurer to handle claims reserving appropriately. Although claims data are time series data, the majority of the proposed (stochastic) claims reserving methods is not based on time series models. Among the time series models, state space models combined with Kalman filter learning algorithms have proven to be very advantageous as they provide high flexibility in modeling and an accurate detection of the temporal dynamics of a system. Against this backdrop, this paper aims to provide a comprehensive review of stochastic claims reserving methods that have been developed and analyzed in the context of state space representations. For this purpose, relevant articles are collected and categorized, and the contents are explained in detail and subjected to a conceptual comparison.

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Construction of Dependence Structure for Rainfall Stations by Joining Time Series Models with Copula Method

Malaysian Journal of Fundamental and Applied Sciences ◽

10.11113/mjfas.v17n4.2345 ◽

2021 ◽

Vol 17 (4) ◽

pp. 306-320

Author(s):

Rahmah Mohd Lokoman ◽

Fadhilah Yusof ◽

Nor Eliza Alias ◽

Zulkifli Yusop

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Dependence Structure ◽

Time Series Models ◽

Stationary Condition ◽

Hydrological Data ◽

Copula Method ◽

Stationary Conditions ◽

Stationarity Conditions

Copula model has applied in various hydrologic studies, however, most analyses conducted does not considering the non-stationary conditions that may exist in the time series. To investigate the dependence structure between two rainfall stations at Johor Bahru, two methods have been applied. The first method considers the non-stationary condition that exists in the data, while the second method assumes stationarity in the time series data. Through goodness-off-fit (GOF) and simulation tests, performance of both methods are compared in this study. The results obtained in this study highlight the importance of considering non-stationarity conditions in the hydrological data.

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Recovering Within-Person Dynamics from Psychological Time Series

10.31234/osf.io/dymhw ◽

2020 ◽

Author(s):

Jonas M B Haslbeck ◽

Oisín Ryan

Keyword(s):

Time Series ◽

Time Series Data ◽

Sampling Frequency ◽

Series Data ◽

Time Series Models ◽

Theoretical Understanding ◽

Psychological Phenomena ◽

Modeling Methodology ◽

Emotion Dynamics ◽

True System

Idiographic modeling is rapidly gaining popularity and promises to tap into the within-person dynamics underlying psychological phenomena. To gain theoretical understanding of these dynamics, we need to make inferences from time series models about the underlying system. Such inferences are subject to two challenges: the time series models will arguably always be misspecified, which means that it is unclear how to make inferences to the underlying system; and second, the sampling frequency must be sufficient to capture the dynamics of interest. We discuss both problems with the following approach: we specify a toy model for emotion dynamics as the true system, generate time series data from it, and then try to recover that system with the most popular time series analysis tools. We show that making straightforward inferences from time series models about an underlying system is difficult. We also show that if the sampling frequency is insufficient, the dynamics of interest cannot be recovered. However, we also show that global characteristics of the system can be recovered reliably. We conclude by discussing the consequences of our findings for idiographic modeling and suggest to adopt a modeling methodology that goes beyond fitting time series models alone.

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The durability of economic indicators in container shipping demand: a case study of East Asia–US container transport

Maritime Business Review ◽

10.1108/mabr-12-2020-0075 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Tomoya Kawasaki ◽

Takuma Matsuda ◽

Yui-yip Lau ◽

Xiaowen Fu

Keyword(s):

Time Series ◽

East Asia ◽

Time Series Data ◽

Economic Indicators ◽

Series Data ◽

Container Shipping ◽

Content Type ◽

The Usa ◽

Container Movement ◽

The Impact

Purpose In the maritime industry, it is vital to have a reliable forecast of container shipping demand. Although indicators of economic conditions have been used in modeling container shipping demand on major routes such as those from East Asia to the USA, the duration of such indicators’ effects on container movement demand have not been systematically examined. To bridge this gap in research, this study aims to identify the important US economic indicators that significantly affect the volume of container movements and empirically reveal the duration of such impacts. Design/methodology/approach The durability of economic indicators on container movements is identified by a vector autoregression (VAR) model using monthly-based time-series data. In the VAR model, this paper can analyze the effect of economic indicators at t-k on container movement at time t. In the model, this paper considers nine US economic indicators as explanatory variables that are likely to affect container movements. Time-series data are used for 228 months from January 2001 to December 2019. Findings In the mainland China route, “building permission” receives high impact and has a duration of 14 months, reflecting the fact that China exports a high volume of housing-related goods to the USA. Regarding the South Korea and Japan routes, where high volumes of machinery goods are exported to the USA, the “index of industrial production” receives a high impact with 11 and 13 months’ duration, respectively. On the Taiwan route, as several types of goods are transported with significant shares, “building permits” and “index of industrial production” have important effects. Originality/value Freight demand forecasting for bulk cargo is a popular research field because of the public availability of several time-series data. However, no study to date has measured the impact and durability of economic indicators on container movement. To bridge the gap in the literature in terms of the impact of economic indicators and their durability, this paper developed a time-series model of the container movement from East Asia to the USA.

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