A Novel Carbon Price Fluctuation Trend Prediction Method Based on Complex Network and Classification Algorithm

Carbon price fluctuation is affected by both internal market mechanisms and the heterogeneous environment. Moreover, it is a complex dynamic evolution process. This paper focuses on carbon price fluctuation trend prediction. In order to promote the accuracy of the forecasting model, this paper proposes the idea of integrating network topology information into carbon price data; that is, carbon price data are mapped into a complex network through a visibility graph algorithm, and the network topology information is extracted. The extracted network topology structure information is used to reconstruct the data, which are used to train the model parameters, thus improving the prediction accuracy of the model. Five prediction models are selected as the benchmark model, and the price data of the EU and seven pilot carbon markets in China from June 19, 2014, to October 9, 2020, are chosen as the sample for empirical analysis. The research finds that the integration of network topology information can significantly improve the price trend prediction of the five benchmark models for the EU carbon market. However, there are great differences in the accuracy improvement effects of China’s seven pilot carbon market price forecasts. Moreover, the forecasting accuracy of the four carbon markets (i.e., Guangdong, Chongqing, Tianjin, and Shenzhen) has improved slightly, but the prediction accuracy of the carbon price trend in Beijing, Shanghai, and Hubei has not improved. We analyze the reasons leading to this result and offer suggestions to improve China’s pilot carbon market.

Validating The Assumptions of Residuals in ARIMA Model for Daily Stock Price Data By using R

Identifying the past data and plannig for future is very important for every organization . Now a days Stock market playes a major role for the development of economy. For the countries economic development, stock market plays a vital role. For this modelling, forecasting is the best way to know the future stock prices based on the past stock prices data. In stock price data, forecasting of closed price plays a major role in financing economic decisions. The Arima model has developed and implemented in many applications .So the researchers utilize arima model in forecasting the closed prices of AMAZON stock price data for future which have been collected from AMAZON 2007-01-03, to 2020-10-12.In this paper the researcher aim is to forecast by using the ARIMA time series model with particular reference to Box and Jenkins approach on daily stock prices of AMAZON With open statistical software R. The validity of ARIMA model is tested by using the standard statistical tests. Keywords: Auto Regressive Integrated Moving Average, Auto Correlation Function, Partial Auto Correlation Function, Akaikae Information Criterion, Auto Regressive Conditional Heteroscedasticity

Comparing Two ARIMA Models for Daily Stock Price Data

Analyzing the past data and planning for future is very important for every public and private organizational decisions. Now a days individuals also using forecasting methods to invest in Stock market. Investments in mutual funds and in registered companies in companies in stock market is the order of the day. In this paper, advanced forecasting methods are fitted to the time related stock price data to study its effectiveness in forecasting future events. Auto correlation and standard models have been analyzed before fitting this model to the above data. The forecasting can be done by using the ARIMA time series(using auto. arima) model. A particular reference have been made to Box and Jenkins approach for day to day stock price data values of Exxon Mobile Corporation from '1995-01-01 to 2020-03-01. With usual statistical software R. Here, ARIMA(1,1,1,) is fitted to this data, These results are compared with the model ARIMA(1,1,1,) by using accuracy measures. Keywords: ARIMA: Auto Regressive Integrated Moving Average ACF: Auto Correlation Function PACF: Partial Auto Correlation Function AIC: Akaikae Information Criterion RMSE: Root mean square error XOM: Exxon Mobil Corporation

Statistical Analysis of New Zealand Electricity Prices: A Risk Manager's Perspective

<p>This thesis considers the conventional SARIMA model and the EVT-GARCH model for forecasting electricity prices. However, we find that these models do not adequately capture the important characteristics of the electricity price data. A new model is developed, the EVT-SARIMA model, for forecasting electricity prices which is found to be the best at modelling the nature of the electricity prices. A time series of half-hourly electricity price data from the Hayward node in New Zealand is transformed into a daily average price series and using this resulting series, appropriate models are fitted for estimating and forecasting. The new EVT-SARIMA model is used to simulate 1000 time series of daily electricity prices, over a 90 day period, to consider strategies for managing the risk associated with price volatility. The effects of different financial instruments on the cumulative distribution functions of predicted revenue obtained using our model are considered. Results suggest that different contracts have different effects on the predicted revenue. However, all contracts have the effect of reducing variability in the predicted revenue values and thus, should be used by a risk manager to reduce the range of probable revenue values. The quantity traded and which contracts to use is dependent on the objectives of the risk manager.</p>

Statistical Analysis of New Zealand Electricity Prices: A Risk Manager's Perspective

<p>This thesis considers the conventional SARIMA model and the EVT-GARCH model for forecasting electricity prices. However, we find that these models do not adequately capture the important characteristics of the electricity price data. A new model is developed, the EVT-SARIMA model, for forecasting electricity prices which is found to be the best at modelling the nature of the electricity prices. A time series of half-hourly electricity price data from the Hayward node in New Zealand is transformed into a daily average price series and using this resulting series, appropriate models are fitted for estimating and forecasting. The new EVT-SARIMA model is used to simulate 1000 time series of daily electricity prices, over a 90 day period, to consider strategies for managing the risk associated with price volatility. The effects of different financial instruments on the cumulative distribution functions of predicted revenue obtained using our model are considered. Results suggest that different contracts have different effects on the predicted revenue. However, all contracts have the effect of reducing variability in the predicted revenue values and thus, should be used by a risk manager to reduce the range of probable revenue values. The quantity traded and which contracts to use is dependent on the objectives of the risk manager.</p>

891. Minimum Manufacturing Costs and National Prices for Weight Loss Treatments, as Potential Mitigation for Anti-Retroviral Related Weight Gain in HIV

Background Weight gain is being observed for a wide range of antiretroviral treatments. Weight gains are higher for people taking first-line integrase inhibitor based treatments, especially those including TAF/FTC. Weight gains are higher for women and people of colour. Clinical obesity increases the risks of cardiovascular disease, diabetes, adverse birth outcomes and could lower survival rates. Anti-obesity treatments are needed to supplement lifestyle interventions and counteract progressive weight gains, but are not routinely provided as part of HIV care. Methods Costs of production for FDA-recommended weight loss treatments and anti-diabetic medications (orlistat, naltrexone-bupropion, topiramate, phentermine, semaglutide, liraglutide and metformin) were estimated using an established and published methodology based on costs of active pharmaceutical ingredients (API), extracted from the global shipping records database Panjiva. This was compared with national drug list price data from a range of low, medium, and high-income countries. Figure 1. Example of methodology for calculating the estimated minimum cost of production for orlistat Results Weight loss and anti-diabetic treatments can be generically manufactured at low per-course costs, e.g. &85 per person per year for oral treatments such as orlistat and &1 per person per month for metformin. However, prices for a year of treatment with orlistat are as high as &1,205 in the USA and as low as &11 in Vietnam. In comparison, a month of ARV treatment costs about &15 via global health institutions like CHAI. Price for injectable (subcutaneous) treatments were higher, ranging from &1,985 for liraglutide in USA to &330 in Morocco, whilst they could potentially be profitably sold for &155 for a 12-week course. No export price data was available for semaglutide. When compared against international list prices, we found wide variations between countries. Table 1. Summary of drug prices and minimum cost estimates Figure 2. Orlistat course costs in a range of countries, compared with estimated minimum cost Figure 3. Liraglutide course costs in a range of countries, compared with estimated minimum cost Conclusion We show that weight loss treatments can be manufactured and sold profitably for low prices, but have a wide price range between countries. Government and non-governmental healthcare systems should be evaluating weight loss agents for inclusion within ART programmes. Disclosures All Authors: No reported disclosures

Analysis of Stock Price Data: Determinition of The Optimal Sliding-Window Length

Over the recent years, the study of time series visualization has attracted great interests. Numerous scholars spare their great efforts to analyze the time series using complex network technology with the intention to carry out information mining. While Visibility Graph and corresponding spin-off technologies are widely adopted. In this paper, we try to apply a couple of models derived from basic Visibility Graph to construct complex networks on one-dimension or multi-dimension stock price time series. As indicated by the results of intensive simulation, we can predict the optimum window length for certain time series for the network construction. This optimum window length is long enough to the majority of stock price SVG whose data length is 1-year. The optimum length is 70% of the length of stock price data series.