scholarly journals Lithuanian electricity market price forecasting model based on univariate time series analysis

Energetika ◽  
2020 ◽  
Vol 66 (1) ◽  
Author(s):  
Mindaugas Česnavičius
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Electricity Market ◽  
Statistical Tests ◽  
Arima Model ◽  
Electricity Price ◽  
Data Set ◽  
Forecasting Models ◽  
Series Analysis ◽  
Univariate Time Series

Electricity price changes can significantly affect expenses in energy intensive industries, adjust profits or losses for electricity retailers and cause problems for country’s national energy strategy implementation. Forecasting models based on statistical methods and previous variable values help to predict future values and adjust strategy according to the forecast. This paper concentrates on the Lithuanian electricity market and presents the widely used ARIMA forecasting models based on the univariate time series analysis. The Lithuanian electricity market is selected due to a lack of statistical researches based on electricity market prices in Lithuania, as well as significant future electricity market liberalization projects. Electricity price data for analysis are taken from the Nord Pool electricity market operator website. The Nord Pool represents the Northern Europe electricity market operator where Lithuania and other 14 European countries trade electricity on a daily basis. To provide a long-term electricity price outlook average monthly data from July 2012 to December 2019 are selected for analysis. Before building the ARIMA model data are tested with various statistical tests to guarantee that time series are stationary, there is no autocorrelation or structural breaks. Once the data validity is confirmed, the time series is divided into train and test sets. The train data set is used to create a fitting ARIMA model, while the test set is used to define forecasting accuracy. Created forecasts of models are compared between each other using common comparison statistics, and the most accurate models are defined. Finally, the selected model is trained on a full dataset and the electricity price forecast for the year 2020 is constructed. The created AR (1) model had the smallest error value compared to the test dataset, while the SARIMA (1,1,1) model had the best approximation statistics. By combining both models the weighted SARIMA (1,1,1) model is constructed with the features of low forecasting error and precise actual time series approximation. The final model forecast for the year 2020 shows the monthly average electricity price decrease at the beginning of the year, a significant increase at the second half of the year and a price drop at the end of the year. Forecasting results can help companies to plan their electricity production and maintenance periods to maximize income from sold energy and minimize potential losses due to planned shutdown.

scholarly journals Forecasting of Economic Indicators (Production, Consumption, Population) of Wheat Crop (A Case Study)

2021 ◽  
Vol 8 (6) ◽  
pp. 38-46
Author(s):  
Mohammad Karim Ahmadzai
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Wheat Crop ◽  
Series Analysis ◽  
The People ◽  
Self Sufficiency ◽  
Univariate Time Series ◽  
Short Run

Wheat is the most important food crop in Afghanistan, whether consumed by the bulk of the people or used in various sectors. The problem is that Afghanistan has a significant shortfall of wheat between domestic production and consumption. Thus, the present study looks at the issue of meeting self-sufficiency for the whole population due to wheat shortages. To do so, we employ time series analysis, which can produce a highly exact short-run prediction for a significant quantity of data on the variables in question. The ARIMA models are versatile and widely utilised in univariate time series analysis. The ARIMA model combines three processes: I the auto-regressive (AR) process, (ii) the differencing process, and (iii) the moving average (MA) process. These processes are referred to as primary univariate time series models in statistical literature and are widely employed in various applications. Where predicting future wheat requirements is one of the most important tools that decision-makers may use to assess wheat requirements and then design measures to close the gap between supply and consumption. The present study seeks to forecast Production, Consumption, and Population for the period 2002-2017 and estimate the values of these variables between 2002 and 2017. (2018-2030).  

scholarly journals STUDY OF THE INFLUENCE OF CHANGING THE PARAMETERS OF THE ARIMA MODEL ON THE QUALITY OF THE FORECAST FOR SHORT DATA SETS

2021 ◽  
Vol 21 (3) ◽  
pp. 36-46
Author(s):  
M.N. Fel’ker ◽  
◽  
V.V. Chesnov
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Arima Model ◽  
Annual Data ◽  
Data Sets ◽  
Arima Models ◽  
Data Set ◽  
Series Analysis ◽  
The Future

Time series, i.e. data collected at various times. The data collection segments may differ de-pending on the task. Time series are used for decision making. Time series analysis allows you to get some result that will determine the format of the decision. Time series analysis was carried out in very ancient times, for example, various calendars became a consequence of the analysis. Later, time series analysis was applied to study and forecast economic, social and other systems. Time se-ries appeared a long time ago. Once upon a time, ancient Babylonian astronomers, studying the po-sition of the stars, discovered the frequency of eclipses, which allowed them to predict their appearance in the future. Later, the analysis of time series, in a similar way, led to the creation of various calen-dars, for example, harvest calendars. In the future, in addition to natural areas, social and economic ones were added. Aim. Search for classification patterns of time series, allowing to understand whether it is possible to apply the ARIMA model for their short-term (3 counts) forecast. Materials and methods. Special software with ARIMA implementation and all need services is made. We examined 59 data sets with a short length and step equal a year, less than 20 values in the paper. The data was processed using Python libraries: Statsmodels and Pandas. The Dickey – Fuller test was used to de-termine the stationarity of the series. The stationarity of the time series allows for better forecasting. The Akaike information criterion was used to select the best model. Recommendations for a rea-sonable selection of parameters for adjusting ARIMA models are obtained. The dependence of the settings on the category of annual data set is shown. Conclusion. After processing the data, four categories (patterns) of year data sets were identified. Depending on the category ranges of parame-ters were selected for tuning ARIMA models. The suggested ranges will allow to determine the starting parameters for exploring similar datasets. Recommendations for improving the quality of post-forecast and forecast using the ARIMA model by adjusting the settings are given.

Time series analysis of COVID-19 cases

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kamalpreet Singh Bhangu ◽  
Jasminder Sandhu ◽  
Luxmi Sapra
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Forecasting ◽  
Time Interval ◽  
Data Set ◽  
Content Type ◽  
Forecasting Models ◽  
Series Analysis ◽  
Auto Regressive ◽  
Death Cases

Purpose This study analyses the prevalent coronavirus disease (COVID-19) epidemic using machine learning algorithms. The data set used is an API data provided by the John Hopkins University resource centre and used the Web crawler to gather all the data features such as confirmed, recovered and death cases. Because of the unavailability of any COVID-19 drug at the moment, the unvarnished truth is that this outbreak is not expected to end in the near future, so the number of cases of this study would be very date specific. The analysis demonstrated in this paper focuses on the monthly analysis of confirmed, recovered and death cases, which assists to identify the trend and seasonality in the data. The purpose of this study is to explore the essential concepts of time series algorithms and use those concepts to perform time series analysis on the infected cases worldwide and forecast the spread of the virus in the next two weeks and thus aid in health-care services. Lower obtained mean absolute percentage error results of the forecasting time interval validate the model’s credibility. Design/methodology/approach In this study, the time series analysis of this outbreak forecast was done using the auto-regressive integrated moving average (ARIMA) model and also seasonal auto-regressive integrated moving averages with exogenous regressor (SARIMAX) and optimized to achieve better results. Findings The inferences of time series forecasting models ARIMA and SARIMAX were efficient to produce exact approximate results. The forecasting results indicate that an increasing trend is observed and there is a high rise in COVID-19 cases in many regions and countries that might face one of its worst days unless and until measures are taken to curb the spread of this disease quickly. The pattern of the rise of the spread of the virus in such countries is exactly mimicking some of the countries of early COVID-19 adoption such as Italy and the USA. Further, the obtained numbers of the models are date specific so the most recent execution of the model would return more recent results. The future scope of the study involves analysis with other models such as long short-term memory and then comparison with time series models. Originality/value A time series is a time-stamped data set in which each data point corresponds to a set of observations made at a particular time instance. This work is novel and addresses the COVID-19 with the help of time series analysis. The inferences of time series forecasting models ARIMA and SARIMAX were efficient to produce exact approximate results.

Recent Mortality Trends in the Spanish Population

2002 ◽  
Vol 8 (4) ◽  
pp. 757-786 ◽  
Author(s):  
A. Felipe ◽  
M. Guillen ◽  
A. M. Perez-Marin
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spanish Population ◽  
Mortality Trends ◽  
Calendar Time ◽  
Series Analysis ◽  
Univariate Time Series ◽  
Using Data ◽  
The Way

ABSTRACTOur research deals with the way that calendar time affects mortality patterns in the Spanish population, and how this information can be used to elaborate predictions. A description of the observed mortality evolution has been worked out using data from 1975 to 1993. We have used Heligman-Pollard Law number two to model the evolution of Spanish mortality over the period and using univariate time series analysis, we have obtained a prognosis for years 1994 to 2010.

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