scholarly journals APPLYING M-ESTIMATES TO DEFINE START VALUE OF EXPONENTIAL AVERAGE IN BRAUN’S FORECAST MODEL OF ZERO LEVEL

2021 ◽  
pp. 150-165
Author(s):  
Александр Анатольевич Васильев
Keyword(s):  
Time Series ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Short Term ◽  
Simple Arithmetic ◽  
Zero Level ◽  
One Step ◽  
Definition Of ◽  
M Estimate ◽  
M Estimates

В экономическом прогнозировании коротких временных рядов часто применяется модель Брауна нулевого порядка. К одной из проблем использования этой модели на первых шагах прогнозирования относится оценка начального значения экспоненциальной средней. Как правило, в качестве такой оценки используется простое среднее арифметическое значение первых уровней ряда, которое является неустойчивой статистической оценкой. Поэтому в данном исследовании предложено для оценки начального значения экспоненциальной средней использовать робастные М-оценки Тьюки, Хампеля, Хьюбера и Эндрюса. Цель исследования заключается в определении целесообразности применения М-оценок для определения начального значения экспоненциальной средней в модели Брауна при прогнозировании коротких временных рядов экономических показателей. В результате проведенного экспериментального исследования установлено: а) к наиболее значимым факторам, влияющим на точность прогноза с использованием модели Брауна, относятся вид временного ряда, значение постоянной сглаживания, отбраковка аномальных уровней и вид весов; б) вид оценки начального значения экспоненциальной средней и число итераций при вычислении М-оценки являются менее значимыми факторами (в связи с этим обоснована целесообразность применения одношаговых М-оценок); в) на начальных шагах прогнозирования при ограниченном количестве уровней временного ряда, когда невозможно достоверно определить вид ряда и когда отсутствуют основания для отбраковки аномальных уровней, предпочтительнее использовать модель Брауна с весами Вейда и определять начальное значение экспоненциальной средней на основе одношаговых робастных М-оценок (в остальных случаях целесообразно применять простое среднее арифметическое значение). In economic forecasting of short-term time series Braun’s model of zero level is often applied. One of issues of usage of this model from the very beginning of forecasting is estimation of start value of exponential average. As usual, simple arithmetic mean of first levels of series, used as such estimate, is volatile statistical estimate. That’s why in this investigation it’s suggested to use Tukey’s, Hampel’s, Huber’s and Andrews’ robust M-estimates for estimation of start value of exponential average. Purpose of research is definition of reasonability of M-estimates application to define start value of exponential average in Braun’s model during forecasting of short-term time series of economic indicators. The results of conducted experimental research are as follows: a) the most important factors, that have significant impact on forecast accuracy with usage of Braun’s model, are type of time series, value of smoothing constant, removal of abnormal levels and type of weights; b) type of estimate of start value of exponential average and quantity of iterations in process of calculation of M-estimate are less significant factors; c) consequently, reasonability of usage of one-step M-estimates is justified; d) on the first steps of forecasting with limited quantity of levels of time series, when it’s impossible to define with certainty type of series and when there is no reasons for removal of abnormal levels, it’s preferable to use Braun’s model with Wade’s weights and define start value of exponential average based on one-step robust M-estimates (in other cases it’s better to use simple arithmetical mean).

Integration of Hydro and Renewable Energy Resources in Energy Planning

2020 ◽  
Author(s):  
Lucero Cynthia Luciano De La Cruz ◽  
Cesar Celis
Keyword(s):  
Renewable Energy ◽  
Energy Transition ◽  
Forecast Model ◽  
Energy Resources ◽  
Energy Planning ◽  
Renewable Energy Resources ◽  
Short Term ◽  
Hydropower Plants ◽  
Forecast Models ◽  
Definition Of

Abstract Renewable energy is the energy obtained from resources inexhaustible in the long term. Furthermore, in some countries, non-conventional renewable energy includes solar, wind, biomass, geothermal and mini-hydropower. The definition of mini-hydropower plants varies depending on the country. As an example, in Peru and Canada, mini-hydropower plants have different installing capacities, below 20MW and 50MW, respectively. Accordingly, this work (i) discusses the Energy Balance and challenges that renewable energies have to face on their way to the energy transition, (ii) highlights the forecast models to generate renewable energy in short-term energy planning. The historical data about the renewable energy resources and the energy produced have been obtained by COES. The R studio software was used for statistical analysis of renewable resources and electricity generation. Also, a forecast model was developed using a neural network to forecast renewable energy generation. The results show a strong correlation between hydro resources and non-conventional renewable energy resources. Finally, the data obtained from the renewable generation forecast model were used as input to carry out a short-term dispatch model using GAMS software to determine the forecast of daily marginal cost in SEIN.

Monthly Load Forecasting Based on Optimum Grey Model

2011 ◽  
Vol 230-232 ◽  
pp. 1226-1230
Author(s):  
Ting Wang ◽  
Xi Miao Jia
Keyword(s):  
Historical Data ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Difficult Problem ◽  
Short Term ◽  
Original Series ◽  
Short Term Load Forecasting ◽  
Calculation Results ◽  
Long Time

Due to the variety and the randomicity of its influencing factors, the monthly load forecasting is a difficult problem for a long time. In order to improve the forecast accuracy, the paper proposes a new load forecast model based on improved GM (1, 1).First, the GM (1, 1) is used to forecast the load data, which takes the longitude historical data as original series, the increment trend of load was forecasted and takes the crosswise historical data as original series, the fluctuation trend of load was forecasted. On this basis the optimum method is led in. An optimal integrated forecasting model is built up. The case calculation results show that the proposed method can remarkably improve the accuracy of monthly load forecasting, and decrease the error. The integrated model this paper describes for short-term load forecasting is available and accurate.

scholarly journals Application of ARIMA and Holt-Winters forecasting model to predict the spreading of COVID-19 for India and its states

2020 ◽  
Author(s):  
Mrutyunjaya Panda
Keyword(s):  
Time Series ◽  
Human Life ◽  
Moving Average ◽  
Predictive Modelling ◽  
Forecast Model ◽  
Vital Role ◽  
Forecasting Model ◽  
Short Term ◽  
Global Threat ◽  
Adf Test

The novel Corona-virus (COVID-2019) epidemic has posed a global threat to human life and society. The whole world is working relentlessly to find some solutions to fight against this deadly virus to reduce the number of deaths. Strategic planning with predictive modelling and short term forecasting for analyzing the situations based on the worldwide available data allow us to realize the future exponential behaviour of the COVID-19 disease. Time series forecasting plays a vital role in developing an efficient forecasting model for a future prediction about the spread of this contagious disease. In this paper, the ARIMA (Auto regressive integrated moving average) and Holt-Winters time series exponential smoothing are used to develop an efficient 20- days ahead short-term forecast model to predict the effect of COVID-19 epidemic. The modelling and forecasting are done with the publicly available dataset from Kaggle as a perspective to India and its five states such as Odisha, Delhi, Maharashtra, Andhra Pradesh and West Bengal. The model is assessed with correlogram, ADF test, AIC and RMSE to understand the accuracy of the proposed forecasting model.

scholarly journals PREDICTIVE ACCURACY OF FUTURES OPTIONS IMPLIED VOLATILITY: THE CASE OF THE EXCHANGE RATE FUTURES MEXICAN PESO-US DOLLAR

2017 ◽  
Vol 5 (9) ◽  
pp. 41
Author(s):  
Guillermo Benavides
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Implied Volatility ◽  
Predictive Accuracy ◽  
Research Effort ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Coefficient Of Determination ◽  
Mexican Peso ◽  
Volatility Forecast

There has been substantial research effort aimed to forecast futures price return volatilities of financial assets. A significant part of the literature shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of several volatility forecast models for the case of the Mexican peso-USD exchange rate futures returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), two option implied volatility models and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. Different to other works in the literature, in this paper there is a more rigorous analysis of the option implied volatilities calculations. The results show that the option implied models are superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-squared-errors. However, the results should be taken with caution given that the coefficient of determination in the regressions was relatively low. According to these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied.

Time Series Features Extraction and Forecast from Multi-feature Stocks with Hybrid Deep Neural Networks.

2020 ◽  
Vol 13 ◽  
Author(s):  
Ye Xu ◽  
Xun Yuan
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Hybrid Model ◽  
Deep Neural Networks ◽  
Arima Model ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Closing Price ◽  
Massive Information ◽  
Better Than

Background: Forecasting of time series stock data is important in financial related works. Stock data usually have multifeatures such as opening price, closing price and so on. The traditional forecast methods, however, is mainly applied to one feature – closing price, or a few, like four or five features. The massive information hidden in the multi-feature data is not thoroughly discovered and used. Objective: Find a method to make used of all information of multi-features and get a forecast model. Method: LSTM based models are introduced in this paper. For comparison, three models are used and they are single LSTM model, hybrid model of LSTM-CNN, and traditional ARIMA model. Results: Experiments with different models are performed on stock data with 50 and 230 features, respectively. Results show that MSE of single LSTM model is 2.4% lower than ARIMA model and MSE of LSTM-CNN model is 12.57% lower than that of single LSTM model on 50 features data. On 230 features data, LSTM-CNN model is found to be improved by 23.41% in forecast accuracy. Conclusion: In this paper, we use three different models – ARIMA, single LSTM and LSTM-CNN hybrid model – to forecast rise and fall of multi-features stock data. It’s found that single LSTM model is better than traditional ARIMA model on the average, and LSTM-CNN hybrid model is better than single LSTM model on 50-feature stock data. What’s more, we use LSTM-CNN model to perform experiments on stock data with 50 and 230 features, respectively. And is found that results of the same model on 230 features data is better than that on 50 features data. It’s proved in our work that the LSTM-CNN hybrid model is better than other models and experiments on stock data with more features could result in better outcomes. We’ll do more works on hybrid models next.

The Short-Term Forecast Model of Pork Price Based on CNN-GA

2012 ◽  
Vol 628 ◽  
pp. 350-358 ◽  
Author(s):  
Zhe Min Li ◽  
Shi Wei Xu ◽  
Li Guo Cui ◽  
Gan Qiong Li ◽  
Xiao Xia Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Basic Structure ◽  
Retail Price ◽  
Practical Significance ◽  
Short Term ◽  
The Neural Network ◽  
High Prediction ◽  
Short Term Forecasting

After analyzing and reviewing the short-term forecasting methods research of pork price at home and abroad, a chaotic neural network model based on genetic algorithm (CNN-GA) was put forward according to the nonlinear characteristics of pork price,which established on the base of the chaotic theory and the neural network technology. Chosen the daily retail price data of the pork (streaky pork) from January 1, 2008 to June 11, 2012,we designed the basic structure of CNN-GA, and thentrainedit in order to attain the trained CNN-GA model. Finally, the trained CNN-GA model was used to predict the 20 days’ (from June 12, 2012 to July 1, 2012) retail price of pork (streaky pork) and then compared the predicted price with the real price to test the model’s forecast accuracy and application ability.The result shows that the model has high prediction precision, good fitting effect and hasan important reference and practical significance for the short-term price forecasting of the pork market.

scholarly journals COMPARING THE PREDICTION ACCURACY OF LSTM AND ARIMA MODELS FOR TIME-SERIES WITH PERMANENT FLUCTUATION

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Ghahreman Abdoli ◽  
Mohsen MehrAra ◽  
Mohammad Ebrahim Ardalani
Keyword(s):  
Time Series ◽  
Economic System ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Economic Time Series ◽  
Intraday Data

In developing countries with an unstable economic system, permanent fluctuation in historical data is always a concern. Recognizing dependency and independency of variables are vague and proceeding a reliable forecast model is more complex than other countries. Although linearization of nonlinear multivariate economic time-series to predict, may give a result, the nature of data which shows irregularities in the economic system, should be ignored. New approaches of artificial neural network (ANN) help to make a prediction model with keeping data attributes. In this paper, we used the Tehran Stock Exchange (TSE) intraday data in 10 years to forecast the next 2 months. Long Short-Term Memory (LSTM) from ANN chooses and outputs compared with the autoregressive integrated moving average (ARIMA) model. The results show, although, in long term prediction, the forecast accuracy of both models reduce, LSTM outperforms ARIMA, in terms of error of accuracy, significantly.

Establishment about Short-Term Demand Predictive Model of Automobile — Based on Extensive Correlation Evaluation and Continuous Time Model

2013 ◽  
Vol 712-715 ◽  
pp. 3123-3128
Author(s):  
Tao Chen ◽  
Zhi Ming Zhu ◽  
Tian Miao Shen
Keyword(s):  
Time Series ◽  
Predictive Model ◽  
Continuous Time ◽  
Short Term ◽  
Continuous Time Model ◽  
Time Model ◽  
Single Time ◽  
Automobile Demand ◽  
Definition Of

The previous models of automobile short-term demand were mainly for single time series. For this disadvantage, the definition of extensive correlation evaluation was proposed, and then the method was discussed to reflect the correlation of factors on automobile demand. Utilizing extensive skills, factors and sub-factors were represented as correlation eigenmatrix which could ensure the level of each factors influences on automobile demand. Accordingly, short-term demand predictive model of automobile was established based on continuous time model.

scholarly journals Penerapan Metode Peramalan untuk Identifikasi Permintaan Konsumen

2020 ◽  
Vol 4 (3) ◽  
pp. 121
Author(s):  
Karina Auliasari ◽  
Mariza Kertaningtyas ◽  
Mawan Kriswantono
Keyword(s):  
Moving Average ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Short Term ◽  
Efficient Planning ◽  
Time Parameters ◽  
Transportation Scheduling ◽  
Using Data ◽  
A Company

The forecast model is done using data from several years before, with the involvement of time parameters in the forecast process is usable for the company to made an effective and efficient planning. Forecasting has an important role because the company requires short-term, medium-term and long-term estimates for each management. For short-term estimates, a company requires personnel, production and transportation scheduling, which is part of the process of scheduling and estimating consumer demand. In this study the results of three forecasting methods were compared, there is simple average, naïve and seasonal naive on demand data of PT SUPER SUKSES NIAGA to be further these three method measured its forecast accuracy using the value of MASE (Mean Absolute Square Error). From the results of data pre-processing consumers whose high value of demand are PT. DIESELINDO, PT. DUTA, PT. HEXINDO and PT. PANATAMA. The results of forecasting shown that the method that has the smallest MASE value is the simple moving average method.

scholarly journals Development of Short-term Flood Forecast Using ARIMA

2021 ◽  
Vol 15 ◽  
pp. 68-75
Author(s):  
Wei Ming Wong ◽  
Mohamad Yusry Lee ◽  
Amierul Syazrul Azman ◽  
Lew Ai Fen Rose
Keyword(s):  
Time Series ◽  
Water Level ◽  
Arima Model ◽  
Information Criterion ◽  
Forecast Model ◽  
Visual Observation ◽  
Stationary Time Series ◽  
Suitable Model ◽  
Flood Forecast ◽  
Short Term

The aim of this study is to use the Box-Jenkins method to build a flood forecast model by analysing real-time flood parameters for Pengkalan Rama, Melaka river, hereafter known as Sungai Melaka. The time series was tested for stationarity using the Augmented Dickey-Fuller (ADF) and differencing method to render a non-stationary time series stationary from 1 July 2020 at 12:00am to 30th July 2020. A utocorrelation (ACF) and partial autocorrelation (PACF) functions was measured and observed using visual observation to identify the suitable model for water level time series. The parameter Akaike Information Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to find the best ARIMA model (BIC). ARIMA (2, 1, 3) was the best ARIMA model for the Pengkalan Rama, with an AIC of 5653.7004 and a BIC of 5695.209. The ARIMA (2, 1, 3) model was used to produce a lead forecast of up to 7 hours for the time series. The model's accuracy was tested by comparing the original and forecast sequences by using Pearson r and R squared. The ARIMA model appears to be adequate for Sungai Melaka, according to the findings of this study. Finally, the ARIMA model provides an appropriate short-term water level forecast with a lead forecast of up to 7 hours. As a result, the ARIMA model is undeniably ideal for river flooding.

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