Time series modelling methods to forecast the volume of self-assessment tax returns in the UK

2021 ◽  
pp. 1-18
Author(s):  
Garo Panikian ◽  
Gabby Colmenares Reverol ◽  
Jayne Rhodes ◽  
Emma McLarnon ◽  
Sarah Keast ◽  
...  
Keyword(s):  
Time Series ◽  
Self Assessment ◽  
Time Series Modelling ◽  
Tax Returns ◽  
The Uk ◽  
Modelling Methods

TIME SERIES MODELLING OF EPIDEMICS: LEADING INDICATORS, CONTROL GROUPS AND POLICY ASSESSMENT

2021 ◽  
Vol 257 ◽  
pp. 83-100
Author(s):  
Andrew Harvey
Keyword(s):  
Time Series ◽  
Data Science ◽  
Leading Indicators ◽  
Control Groups ◽  
Balanced Growth ◽  
Time Series Modelling ◽  
Key Variables ◽  
The Uk ◽  
The Relationship

This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020, Harvard Data Science Review, Special Issue 1—COVID-19, https://hdsr.mitpress.mit.edu/pub/ozgjx0yn) is extended to model the relationship between two or more series and the role of common trends is discussed. Data on daily deaths from COVID-19 in Italy and the UK provides an example of leading indicators when there is a balanced growth. When growth is not balanced, the model can be extended by including a non-stationary component in one of the series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy in the spring of 2020.

Has the SARS-CoV-2 outbreak had an impact on uptake of digital cessation support in UK smokers? An interrupted time series analysis (Preprint)

2020 ◽  
Author(s):  
Olga Perski ◽  
Aleksandra Herbec ◽  
Lion Shahab ◽  
Jamie Brown
Keyword(s):  
Time Series ◽  
Smoking Cessation ◽  
Secondary Analysis ◽  
Interrupted Time Series ◽  
Step Change ◽  
Outcome Variable ◽  
Primary Analysis ◽  
Increasing Trend ◽  
The Uk ◽  
Temporary Closure

BACKGROUND The SARS-CoV-2 outbreak may motivate smokers to attempt to stop in greater numbers. However, given the temporary closure of UK stop smoking services and vape shops, smokers attempting to quit may instead seek out digital support, such as websites and smartphone apps. OBJECTIVE We examined, using an interrupted time series approach, whether the SARS-CoV-2 outbreak has been associated with a step change or increasing trend in UK downloads of an otherwise popular smoking cessation app, Smoke Free. METHODS Data were from daily and non-daily adult smokers in the UK who had downloaded the Smoke Free app between 1 January 2020 and 31 March 2020 (primary analysis) and 1 January 2019 and 31 March 2020 (secondary analysis). The outcome variable was the number of downloads aggregated at the 12-hourly (primary analysis) or daily level (secondary analysis). The explanatory variable was the start of the SARS-CoV-2 outbreak, operationalised as 1 March 2020 (primary analysis) and 15 January 2020 (secondary analysis). Generalised Additive Mixed Models adjusted for relevant covariates were fitted. RESULTS Data were collected on 45,105 (primary analysis) and 119,881 (secondary analysis) users. In both analyses, there was no evidence for a step change or increasing trend in downloads attributable to the start of the SARS-CoV-2 outbreak. CONCLUSIONS In the UK, between 1 January 2020 and 31 March 2020, and between 1 January 2019 and 31 March 2020, there was no evidence that the SARS-CoV-2 outbreak has been associated with a surge in downloads of a popular smoking cessation app. CLINICALTRIAL osf.io/zan2s

scholarly journals Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Miguel Ángel Ruiz Reina
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prognostic Models ◽  
Series Data ◽  
Economic Agents ◽  
Time Series Modelling ◽  
Simulation And Training ◽  
Uncertainty Method ◽  
And Training ◽  
Hotel Accommodation

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions.

scholarly journals Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
C. W. Tian ◽  
H. Wang ◽  
X. M. Luo
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Moving Average ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Annual Peak ◽  
Time Series Modelling ◽  
Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

TQM Self‐assessment in the UK

1994 ◽  
Vol 6 (4) ◽  
pp. 56-61 ◽  
Author(s):  
Mark Finn ◽  
Leslie J. Porter
Keyword(s):  
Self Assessment ◽  
The Uk
Sign in / Sign up

Export Citation Format

Share Document