scholarly journals Recovering Within-Person Dynamics from Psychological Time Series

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.

A UNIFIED APPROACH TO THE MEASUREMENT ERROR PROBLEM IN TIME SERIES MODELS

2002 ◽  
Vol 18 (2) ◽  
pp. 278-296 ◽  
Author(s):  
Katsuto Tanaka
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Measurement Error ◽  
Long Memory ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Models ◽  
Unified Approach ◽  
Underlying Process ◽  
Short Memory

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.

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

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.

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

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.

scholarly journals Stochastic Claims Reserving Methods with State Space Representations: A Review

Risks ◽  
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.

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

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.

scholarly journals Likelihood Inference for Generalized Integer Autoregressive Time Series Models

Econometrics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 43 ◽  
Author(s):  
Harry Joe
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Probability Generating Function ◽  
Likelihood Estimation ◽  
Model Fit ◽  
Likelihood Inference ◽  
Series Data ◽  
Time Series Models ◽  
Autoregressive Time Series ◽  
The Past

For modeling count time series data, one class of models is generalized integer autoregressive of order p based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distribution of an observation given the past p observations. Two data examples are included and show that thinning operators based on compounding can substantially improve the model fit compared with the commonly used binomial thinning operator.

scholarly journals Predicting Total Business Sales using Time Series Analysis

2020 ◽  
pp. 475-482
Author(s):  
Navya Sri Kalli ◽  
Harsha Teja Pullagura
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Time Series Models ◽  
Key Factors ◽  
Sales Data ◽  
Grand Total ◽  
Simple Exponential Smoothing ◽  
Total Sales

aEconomic activity undergoes 4 phases (expansion, peak, contraction, trough/recession) in which recession is a period of lowest activity and peak indicates the highest activity. Total Business sales is one of the key factors that influence the economic activity of a country. Total sales or gross sales is the grand total of all sales revenues a business generates from normal activities. The frequency of time series sales data can be monthly, quarterly, or annually. Prediction of business sales is highly important as it determines various factors in the market including Gross Domestic Product (GDP). The algorithms or models required for prediction of time series data are different from other machine learning models. Since sales is affected by time, a time series data should be stationary. Only when the data is stationarized, we can apply the algorithms on them. In this paper, monthly sales data is collected and predictions are done using moving average, simple exponential smoothing, Holt’s model, ARIMA, and SARIMAX. Root Mean Square(RMS) is the accuracy metric of time series models and lower RMS indicates higher accuracy. In this paper, a lower value of RMS is obtained for the SARIMAX model.

scholarly journals Forecasting Wheat Production Using Time Series Models in Pakistan

2019 ◽  
Vol 8 (2) ◽  
pp. 172-177
Author(s):  
M Asif Masood ◽  
Irum Raza ◽  
Saleem Abid
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Linear Quadratic ◽  
Wheat Production ◽  
S Curve ◽  
Double Exponential Smoothing

The present paper was designed to forecast wheat production for 2017-18, 2018-19 and 2019-2020 respectively by using time series data from 1971-72 to 2016-17 with best selected time series models. Linear, Quadratic, Exponential, S-Curve, Double Exponential Smoothing, Single exponential smoothing, Moving average and ARIMA were estimated for wheat production. The results showed a mix trend in production of wheat for selected time period. ARIMA (2,1,2) was found best one keeping in view close forecasts with actual reported wheat production. So the preference inclined towards the ARIMA (2,1,2) than quadratic to forecasts of wheat production.

On diagnostics in conditionally heteroskedastic time series models under elliptical distributions

2004 ◽  
Vol 41 (A) ◽  
pp. 393-405 ◽  
Author(s):  
Shuangzhe Liu
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Time Series Data ◽  
Financial Time Series ◽  
Local Influence ◽  
Series Data ◽  
Time Series Models ◽  
Elliptical Distributions ◽  
Financial Time ◽  
Likelihood Displacement

In statistical diagnostics and sensitivity analysis, the local influence method plays an important rôle. In the present paper, we use this method to study financial time series data and conditionally heteroskedastic models under elliptical distributions. We start with a likelihood displacement, and consider data- and model-perturbation schemes. We obtain corresponding matrices of derivatives, and measures of slope and normal curvature, and then discuss the assessment of local influence.

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