Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

2019 ◽  
Vol 13 (3) ◽  
pp. 135-144
Author(s):  
Sasmita Hayoto ◽  
Yopi Andry Lesnussa ◽  
Henry W. M. Patty ◽  
Ronald John Djami
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Significant Parameter ◽  
International Airport ◽  
Forecast Time

The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

scholarly journals Comparison of Indonesian Imports Forecasting by Limited Period Using SARIMA Method

2019 ◽  
Vol 2 (2) ◽  
pp. 90
Author(s):  
Harits Ar Rosyid ◽  
Mutyara Whening Aniendya ◽  
Heru Wahyu Herwanto ◽  
Peizhi Shi
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Market Price ◽  
Forecast Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Strategic Plans ◽  
The Government ◽  
Forecast Time ◽  
High Degree

The development of Indonesia's imports fluctuate over years. Inability to anticipate such rapid changes can cause economic slump due to inappropriate policy. For instance, recent years imports in rice led to the extermination of rice reserves. The reason is to maintain the market price of rice in Indonesia. To overcome these changes, forecasting the amount of imports should assist the Government in determining the optimum policy. This can be done by utilizing an algorithm to forecast time series data, in this case the amount of imports in the next few months with a high degree of accuracy. This study uses data obtained from the official website of the Indonesian Ministry of Trade. Then, Seasonal Autoregressive Integrated Moving Average (SARIMA) method is applied to forecast the imports. This method is suitable for the interconnected dependent variables, as well as in forecasting seasonal data patterns. The results of the experiment showed that 6-period forecast is the most accurate results compared to forecasting by 16 and 24 periods. The research resulted in the best model, that is ARIMA (0, 1, 3)(0, 1, 1)12 produces forecasting with a MAPE value of 7.210 % or an accuracy rate of 92.790 %. By applying this imports forecast model, the government can have a forward strategic plans such as selectively imports products and carefully decide the amount of the incoming products to Indonesia. Hence, it could maintain or improve the economic condition where local businesses can grow confidently. 

scholarly journals Subsequence Time Series Clustering

2011 ◽  
pp. 1871-1876
Author(s):  
Jason Chen
Keyword(s):  
Time Series ◽  
Traditional Method ◽  
Time Series Data ◽  
Large Data ◽  
Series Data ◽  
Data Sets ◽  
Data Set ◽  
Time Series Clustering ◽  
Mining Community ◽  
Representative Points

Clustering analysis is a tool used widely in the Data Mining community and beyond (Everitt et al. 2001). In essence, the method allows us to “summarise” the information in a large data set X by creating a very much smaller set C of representative points (called centroids) and a membership map relating each point in X to its representative in C. An obvious but special type of data set that one might want to cluster is a time series data set. Such data has a temporal ordering on its elements, in contrast to non-time series data sets. In this article we explore the area of time series clustering, focusing mainly on a surprising recent result showing that the traditional method for time series clustering is meaningless. We then survey the literature of recent papers and go on to argue how time series clustering can be made meaningful.

scholarly journals Multifraktalitas dan Studi Komparatif Prediksi Indeks dengan Metode Arima dan Artificial Neural Network (ANN)

The Winners ◽  
2008 ◽  
Vol 9 (2) ◽  
pp. 112
Author(s):  
Harjum Muharam ◽  
Muhammad Panji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Moving Average ◽  
Back Propagation ◽  
Series Data ◽  
Future Value ◽  
Auto Regressive ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper discusses technical analysis widely used by investors. There are many methods that exist and used by investor to predict the future value of a stock. In this paper we start from finding the value of Hurst (H) exponent of LQ 45 Index to know the form of the Index. From H value, we could determinate that the time series data is purely random, or ergodic and ant persistent, or persistent to a certain trend. Two prediction tools were chosen, ARIMA (Auto Regressive Integrated Moving Average) which is the de facto standard for univariate prediction model in econometrics and Artificial Neural Network (ANN) Back Propagation. Data left from ARIMA is used as an input for both methods. We compared prediction error from each method to determine which method is better. The result shows that LQ45 Index is persistent to a certain trend therefore predictable and for outputted sample data ARIMA outperforms ANN.

Improving discretization based pattern discovery for multivariate time series by additional preprocessing

2021 ◽  
Vol 25 (5) ◽  
pp. 1051-1072
Author(s):  
Fabian Kai-Dietrich Noering ◽  
Konstantin Jonas ◽  
Frank Klawonn
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Pattern Discovery ◽  
Computing Time ◽  
Real Life ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Recurrent Patterns

In technical systems the analysis of similar load situations is a promising technique to gain information about the system’s state, its health or wearing. Very often, load situations are challenging to be defined by hand. Hence, these situations need to be discovered as recurrent patterns within multivariate time series data of the system under consideration. Unsupervised algorithms for finding such recurrent patterns in multivariate time series must be able to cope with very large data sets because the system might be observed over a very long time. In our previous work we identified discretization-based approaches to be very interesting for variable length pattern discovery because of their low computing time due to the simplification (symbolization) of the time series. In this paper we propose additional preprocessing steps for symbolic representation of time series aiming for enhanced multivariate pattern discovery. Beyond that we show the performance (quality and computing time) of our algorithms in a synthetic test data set as well as in a real life example with 100 millions of time points. We also test our approach with increasing dimensionality of the time series.

scholarly journals On Modeling Murder Crimes in Nigeria

2018 ◽  
pp. 157-162
Author(s):  
Obubu Maxwell ◽  
Ikediuwa Udoka Chinedu ◽  
Anabike Charles Ifeanyi ◽  
Nwokike Chukwudike C
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Data Set ◽  
Adequate Model ◽  
Resource Center ◽  
Moving Average Models ◽  
Augmented Dickey Fuller ◽  
Auto Regressive

This paper examines the modelling and forecasting Murder crimes using Auto-Regressive Integrated Moving Average models (ARIMA). Twenty-nine years data obtained from Nigeria Information Resource Center were used to make predictions. Among the most effective approaches for analyzing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). The augmented Dickey-Fuller test for unit root was applied to the data set to investigate for Stationarity, the data set was found to be non-stationary hence transformed using first-order differencing to make them Stationary. The Stationarities were confirmed with time series plots. Statistical analysis was performed using GRETL software package from which, ARIMA (0, 1, 0) was found to be the best and adequate model for Murder crimes. Forecasted values suggest that Murder would slightly be on the increase.

scholarly journals Covid-19 Pandemic: Application of Machine Learning Time Series Analysis for Prediction of Human Future

2020 ◽  
Author(s):  
Vikas Chaurasia ◽  
Saurabh Pal
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Linear Trend ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Human Society ◽  
Data Set ◽  
Naive Method ◽  
The Government

Abstract Purpose:Coronavirus disease is an irresistible infection caused by the respiratory disease Coronavirus 2 (SARS-CoV-2). It was first found in Wuhan, China, in December 2019, and has since spread universally, causing a constant pandemic. On June 3, 2020, 6.37 million cases were found in 188 countries and regions. Prevention is the only cure for this disease. A study was carried out on Coronavirous to observe the number of cases, deaths and recovery cases worldwide within a specific time period of five months. Based on this data, this research paper will predict the future spread of this infectious disease in human society. Methods:In our study, the data set was taken from WHO "Data WHO Coronavirus Covid-19 cases and deaths-WHO-COVID-19-global-data". This dataset contains information about the observation date, provenance/state, country/region and latest updates. In this article, we implemented several forecasting techniques: naive method, simple average, moving average, single exponential smoothing, Holt linear trend method, Holt Winter method and ARIMA, for comparison, and how these methods improve the Root mean square error score.Results:The naive method is best suited as described over all other methods. In the ARIMA model, utilizing grid search, we recognized a lot of boundaries that delivered the best-fit model for our time series data. By continuing the model, future predictions of death cases indicate that the number of deaths will increased by more than 600,000 by January 2020.Conclusion:This survey will support the government and experts in making arrangements for what is about to happen. Based on the findings of instantaneous model, these models can be adjusted to guide long time.

Forecast of COVID-19 Cases in Nigeria: An implementation of an ARIMA model (Preprint)

2020 ◽  
Author(s):  
Sanyaolu Ameye ◽  
Michael Awoleye ◽  
Emmanuel Agogo ◽  
Ette Etuk
Keyword(s):  
Predictive Model ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Epidemiological Data ◽  
Series Data ◽  
Global Pandemic ◽  
First Case ◽  
Epidemiological Trend ◽  
Data Points

BACKGROUND The Coronavirus disease 2019 (COVID-2019) is a global pandemic and Nigeria is not left out in being affected. Though, the disease is just over three months since first case was identified in the country, we present a predictive model to forecast the number of cases expected to be seen in the country in the next 100 days. OBJECTIVE To implement a predictive model in forecasting the near future number of positive cases expected in the country following the present trend METHODS We performed an Auto Regressive Integrated Moving Average (ARIMA) model prediction on the epidemiological data obtained from Nigerian Centre for Disease Control to predict the epidemiological trend of the prevalence and incidence of COVID-2019. RESULTS There were 93 time series data points which lacked stationarity. From our ARIMA model, it is expected that the number of new cases declared per day will keep rising and towards the early September, 2020, Nigeria is expected to have well above sixty thousand confirmed cases. CONCLUSIONS We however believe that as we have more data points our model will be better fine-tuned.

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