Modelling rein tension during riding sessions using the generalised additive modelling technique

2018 ◽  
Vol 14 (4) ◽  
pp. 209-221 ◽  
Author(s):  
A. Egenvall ◽  
A. Byström ◽  
L. Roepstorff ◽  
M. Rhodin ◽  
M. Eisersiö ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Stride Length ◽  
Time Series Data ◽  
Training Session ◽  
Series Data ◽  
Video Recordings ◽  
Exploratory Technique ◽  
Tension Curve ◽  
Using Data ◽  
The Right

General additive modelling (GAM-modelling) is an exploratory technique that can be used on longitudinal (time series) data, e.g. rein tension, over a period of time. The aim was to apply GAM-modelling to investigate changes in rein tension during a normal flatwork training session. Six riders each rode two or three of their horses (n=17 horses) during a normal flatwork/dressage training session with video recordings and rein tension measurements (128 Hz). Training sessions were classified according to rider position, stride length and whether horses were straight, bent to the left or bent to the right. The rein tension data were split into strides and for each stride minimal (MIN) and maximal (MAX) rein tension were determined and the area under the rein tension curve (AUC) was calculated. Using data on a contact the three outcome variables MIN, MAX and AUC rein tension were modelled by horse and rein (left/right), and time within the session was modelled as a smooth function. Two additional sets of models were constructed; one set using data within-rein with gait as a fixed effect and one set with rein and gait as fixed effects. Mean ± standard deviation values were MIN: 8.0±7.7 N, AUC: 180±109 Ns, and MAX: 49±31 N. GAM-modelling extracted visually interpretable information from the originally chaotic rein tension signals. Modelled data suggest that MIN, AUC and MAX follow the same pattern within horse. In general, rein tension was lowest in walk, intermediate in trot and highest in canter. Evaluating the entire ride, 12/17 horses systematically showed higher tension in the right rein. It is concluded that GAM-models may be useful for detecting patterns through time in biomechanical data.

ANALISA PREDIKSI INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN METODE ARIMA

2020 ◽  
Vol 2 (2) ◽  
pp. 454
Author(s):  
Julkifli Purnama ◽  
Ahmad Juliana
Keyword(s):  
Capital Market ◽  
Stock Price ◽  
Time Series Data ◽  
Arima Model ◽  
Secondary Data ◽  
Actual Data ◽  
Series Data ◽  
Analysis Technique ◽  
The Right ◽  
The Capital Market

Investment in the capital market every manager needs to analyze to make decisions so that the right target to produce profits in accordance with what is expected. For that, we need a way to predict the decisions that will be taken in the future. The research objective is to find the best model and forecasting of the composite stock price index (CSPI). Data analysis technique The ARIMA Model time series data from historical data is the basis for forecasting. Secondary data is the closing price of the JCI on July 16 2018 to July 16 2019 to see how accurate the forecasting is done on the actual data at that time. The results of the study that the best Arima model is Arima 2.1.2 with an R-squared value of 0.014500, Schwarz criterion 10.83497 and Akaike info criterion of 10.77973. Results of forecasting actual data are 6394,609, dynamic forecast 6387,551 selisish -7,05799, statistics forecas 6400,653 difference of 6,043909. For investors or the public can use the ARIMA method to be able to predict or predict the capital market that will occur in the next period.

ANALISIS PERMINTAAN BERAS DI SUMATERA UTARA

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Hasyrul Aziz Harahap
Keyword(s):  
Time Series ◽  
Fixed Effects ◽  
Time Series Data ◽  
Food Prices ◽  
Series Data ◽  
Cross Point ◽  
Social Unrest ◽  
Corn Prices ◽  
North Sumatra ◽  
Global Food

Indonesia is often categorized as low food resilient nation, in the sense vulnerable to social unrest and rising global food prices. Where every year the number of requests or local domestic rice continue to increase along with the increasing number of people. This study aims to look at and determine how much influence the price of rice, corn prices and the number of population and GDP of the demand for rice in North Sumatra. Used in measuring and analyzing time series data (time series) and the cross-point (cross section) of the 25 districts / municipalities in the period from 2005 to 2010. Data analysis using fixed effects (fixed effect). The results showed a significant effect between the price of rice, the population and GDP of the demand for rice in North Sumatra. While corn prices do not influence of the demand for rice in North Sumatra. The magnitude of the effect is shown by the coefficient of independent variables, namely: -5.215489 for the variable price of rice, 13.08473 for the population, 4.736669 for the variable GDP.

scholarly journals Effectiveness and Forecasting of Interest Rate Reversal BI 7-Day Repo Rate in Indonesia: Lower Bound on Monetary Policy?

2018 ◽  
Vol 9 (1) ◽  
pp. 171-180
Author(s):  
I Gede Sanica ◽  
I Ketut Nurcita ◽  
I Made Mastra ◽  
Desak Made Sukarnasih
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Implementation Of Monetary Policy ◽  
Repo Rate ◽  
The Right ◽  
The Impact

AbstractThis study aims to analyze effectivity and forecast of interest rate BI 7-Day Repo Rate as policy reference in the implementation of monetary policy. The method was used in this study contains Vector Autoregression (VAR) to estimate effectivity of BI 7-Day Repo Rate and Autoregressive Integrated Moving Average (ARIMA) to forecast of BI 7-Day Repo Rate. Period of observation in this study used time series data during 2016.4 until 2017.6. The result of this research shows that the transformation of the BI Rate to BI 7-Day Repo Rate is the right step in the monetary policy operation in the effort to reach deepening of the financial market and strengthen the interbank money market structure so that it will decrease loan interest rate and encourage credit growth. The effectiveness of the use of BI 7 Day-Repo Rate on price stability is indicated by the positive relationship between the benchmark interest rate and inflation compared to the BI Rate. The impact of BI 7-Day Repo Rate on economic growth that tends to be positive. Forecasting the use of BI 7-Day Repo Rate shows good results with declining value levels, so this will encourage deepening the financial markets.

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

scholarly journals Effect of Economic Integration on Agricultural Export Performance in Selected West African Countries

Economies ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 79
Author(s):  
Olatunji Abdul Shobande
Keyword(s):  
Economic Integration ◽  
Fixed Effects ◽  
Agricultural Sector ◽  
Time Series Data ◽  
Agricultural Research ◽  
Export Performance ◽  
Strong Predictor ◽  
West African ◽  
Series Data ◽  
Agricultural Export

The paper investigates the effect of economic integration on agricultural export performance in West African economies using the gravity model of bilateral trade on the annual time series data straddling the period 1970 to 2016. The empirical evidence is based on the pooled OLS and fixed effects estimator. We find that economic integration, as measured by trade openness, is a remarkably strong predictor of export performance in the region. We also examine the effect of geographical distance measured by effective nominal exchange rates and we find it has a negative effect on agricultural export performance. The paper recommends the adoption of a common currency to help mitigate exchange rate negativity that serves as resistance to trade in the region. Likewise, proactive agricultural research, extension and market driven strategies are strongly advocated for driven competition and economic efficiency within the regional agricultural sector.

Intercomparison of Seastate and Zerocrossing Parameters From the WACSIS Field Experiment and Interpretation Using Video Evidence

2002 ◽  
Author(s):  
Stephen F. Barstow ◽  
Harald E. Krogstad ◽  
Lasse Lo̸nseth ◽  
Jan Petter Mathisen ◽  
Gunnar Mo̸rk ◽  
...  
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Field Experiment ◽  
Time Series Data ◽  
Series Data ◽  
Sea State ◽  
Single Wave ◽  
Video Recordings ◽  
Wave Parameters ◽  
Wave Elevation

During the WACSIS field experiment, wave elevation time series data were collected over the period December 1997 to May 1998 on and near the Meetpost Nordwijk platform off the coast of the Netherlands from an EMI laser, a Saab radar, a Baylor Wave Staff, a Vlissingen step gauge, a Marex radar and a Directional Waverider. This paper reports and interprets, with the help of simultaneous dual video recordings of the ocean surface, an intercomparison of both single wave and sea state wave parameters.

scholarly journals Forecasting the Spread of COVID-19 Pandemic with Prophet

2021 ◽  
Vol 35 (2) ◽  
pp. 115-122
Author(s):  
Mohan Mahanty ◽  
K. Swathi ◽  
K. Sasi Teja ◽  
P. Hemanth Kumar ◽  
A. Sravani
Keyword(s):  
Data Science ◽  
Statistical Data ◽  
Time Series Data ◽  
Daily Basis ◽  
Series Data ◽  
Use Case ◽  
The Past ◽  
Open Source Framework ◽  
Forecast Models ◽  
Using Data

COVID-19 pandemic shook the whole world with its brutality, and the spread has been still rising on a daily basis, causing many nations to suffer seriously. This paper presents a medical stance on research studies of COVID-19, wherein we estimated a time-series data-based statistical model using prophet to comprehend the trend of the current pandemic in the coming future after July 29, 2020 by using data at a global level. Prophet is an open-source framework discovered by the Data Science team at Facebook for carrying out forecasting based operations. It aids to automate the procedure of developing accurate forecasts and can be customized according to the use case we are solving. The Prophet model is easy to work because the official repository of prophet is live on GitHub and is open for contributions and can be fitted effortlessly. The statistical data presented on the paper refers to the number of daily confirmed cases officially for the period January 22, 2020, to July 29, 2020. The estimated data produced by the forecast models can then be used by Governments and medical care departments of various countries to manage the existing situation, thus trying to flatten the curve in various nations as we believe that there is minimal time to do this. The inferences made using the model can be clearly comprehended without much effort. Furthermore, it tries to give an understanding of the past, present, and future trends by showing graphical forecasts and statistics. Compared to other models, prophet specifically holds its own importance and innovativeness as the model is fully automated and generates quick and precise forecasts that can be tunable additionally.

scholarly journals OPEN DEFECATION IN MALAWI: A BOX-JENKINS ARIMA APPROACH

2020 ◽  
Vol 5 ◽  
pp. 156-165
Author(s):  
Smartson. P. NYONI ◽  
Thabani NYONI
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Annual Number ◽  
Open Defecation ◽  
Sanitation And Hygiene ◽  
Vision 2030 ◽  
Annual Time Series ◽  
The Government ◽  
The Right ◽  
Annual Time

Using annual time series data on the number of people who practice open defecation in Malawi from 2000 – 2017, the study predicts the annual number of people who will still be practicing open defecation over the period 2018 – 2021. The study applies the Box-Jenkins ARIMA methodology. The diagnostic ADF tests show that the M series under consideration is an I (1) variable. Based on the AIC, the study presents the ARIMA (3, 1, 0) model as the optimal model. The diagnostic tests further show that the presented model is stable and its residuals are stationary in levels. The results of the study indicate that the number of people practicing open defecation in Malawi is likely to decline, over the period 2018 – 2022, from approximately 5.1% to almost 2.8% of the total population. Indeed, by 2030, open defecation can be eliminated in Malawi: hence, the country is in the right track with regards to its vision 2030 (on water, sanitation and hygiene). The study suggested a 3-fold policy recommendation to be put into consideration, especially by the government of Malawi.

scholarly journals Forecasting the Number of Coronavirus (COVID-19) Cases in Ethiopia Using Exponential Smoothing Times Series Model

2020 ◽  
Author(s):  
Teshome Hailemeskel Abebe
Keyword(s):  
Time Series Data ◽  
Exponential Smoothing ◽  
Series Data ◽  
Smoothing Methods ◽  
Times Series ◽  
Double Exponential ◽  
Forecasting Performance ◽  
Double Exponential Smoothing ◽  
The Right ◽  
Exponential Smoothing Method

AbstractThe main objective of this study is to forecast COVID-19 case in Ethiopiausing the best-fitted model. The time series data of COVID-19 case in Ethiopia from March 14, 2020 to June 05, 2020 were used.To this end, exponential growth, single exponential smoothing method, and doubleexponential smoothing methodwere used. To evaluate the forecasting performance of the model, root mean sum of square error was used. The study showed that double exponential smoothing methods was appropriate in forecasting the future number ofCOVID-19 cases in Ethiopia as dictated by lowest value of root mean sum of square error. The forecasting model shows that the number of coronavirus cases in Ethiopia grows exponentially. The finding of the results would help the concerned stakeholders to make the right decisions based on the information given on forecasts.

scholarly journals Detection of Outliers in the Volatility of Malaysia Shariah Compliant Index Return: The Impulse Indicator Saturation Approach

2020 ◽  
pp. 1-7
Author(s):  
Ida Normaya Mohd Nasir ◽  
Mohd Tahir Ismail
Keyword(s):  
Time Series Data ◽  
Global Financial Crisis ◽  
Financial Time Series ◽  
The United States ◽  
Series Data ◽  
Crude Oil Prices ◽  
Global Events ◽  
Using Data ◽  
Impulse Indicator Saturation ◽  
Detection Of Outliers

Financial time series data often affected by various unexpected events which known as the outliers. The aim of this study is to detect the outliers in high frequency data using Impulse Indicator Saturation approach (IIS).Monte Carlo simulations illustrate the ability of IIS to detect outliers by using data with various simulation settings. For empirical application, we have chosen the Malaysia Shariah compliant index which is the FBM EMAS Shariah (FBMS) index. The result of this study discovered the presence of 47 outliers which related to several global events such as global financial crisis (2008 & 2009), the falling of stock market (2011), the United States debt-ceiling crisis (2013) and the declination of international crude oil prices (2014). Keywords: outliers; volatility; stock indices; IIS

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