Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach

Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequency analysis, which can adapt to various non-stationarity signals relevant for enhancing forecasting performance in the era of big data. To this extent, we employ variants of mode decomposition-based extreme learning machines namely: (i) Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based ELM Model (CEEMDAN-ELM), (ii) Ensemble Empirical Mode Decomposition-based ELM Model (EEMD-ELM) and (iii) Empirical Mode Decomposition Based ELM Model (EMD-ELM), which cut-across soft computing and artificial intelligence to analyze multi-commodity time series data by decomposing them into seven independent intrinsic modes and one residual with varying frequencies that depict some interesting characterization of price volatility. Our findings show that in terms of the model-specific forecast accuracy measures different dynamics in the two scenarios namely the (non) COVID periods. However, the introduction of a benchmark, namely the autoregressive integrated moving average model (ARIMA) reveals a slight change in the earlier dynamics, where ARIMA outperform our proposed models in the Japan gas and the US gas markets. To check the superiority of our models, we apply the model-confidence set (MCS) and the Kolmogorov-Smirnov Predictive Ability test (KSPA) with more preference for the former in a multi-commodity framework, which reveals that in the pre-COVID era, CEEMDAN-ELM shows persistence and superiority in accurately forecasting Crude oil, Japan gas, and US gas. Nonetheless, this paradigm changed during the COVID-era, where CEEMDAN-ELM favored Japan gas, US gas, and coal market with different rankings via the Model confidence set evaluation methods. Overall, our numerical experiment indicates that all decomposition-based extreme learning machines are superior to the benchmark model.

Fusion in Cryptocurrency Price Prediction: A Decade Survey on Recent Advancements, Architecture, and Potential Future Directions

Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However, Dash coin is an individual cryptocurrency, but it is derived from Bitcoin and Litecoin. The change in Bitcoin and Litecoin prices affects the Dash coin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction of Dash coin through the past days’prices of Dash, Litecoin, and Bitcoin as they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations. Author

Automakers Stock Price Movement Comparison under COVID-19

In this paper, we compare global automobile manufacturing companies' stock price movement under the pandemic in 2020. The purpose of this work is to investigate the stock price movement of top automobile manufacturing companies. Here, we used machine learning based time series data clustering method. We considered the period of time series stock data from 2020/01/02 to 2021/03/18. In March 2020, around the world, the worst stock price plunge was caused by COVID-19. Then almost all global automakers' stock prices were severely damaged. They, however, recovered gradually their stock prices. On the stock prices, investors' expectations are reflected. The recovery pattern of stock prices can mean the investors' evaluation of the companies. The result of the clustering, contrary to our expectations, shows that the stock prices were likely to move depending on the country, instead of individual companies' performance. The country-based clusters we found are a Japanese companies' cluster, two USA companies' clusters, and two Chinese companies' clusters. In addition, two regional clusters were found which are Asian region cluster and EU region cluster. In the paper we will describe the differences of stock movement patterns among the country-based clusters. © 2021 IEEE.

Stock Price Analysis and Prediction

Covid has taught a valuable lifelong lesson. During the pandemic, economies of countries collapsed and many nations had to undergo a complete lockdown. Individuals lost their sources of income and their savings dwindled trying to survive the lockdown. Many small-scale industries closed down for not being able to recover losses. Despite of the economic machine being slowed;the cog of stock market ran smoothly. The moral learnt was one must have multiple sources of income. During lockdown, the stock market collapsed hard. Now a year later, the market is stronger than before and has achieved new benchmarks. Stock market is erratic and most people relate it to gambling. There are other ways to invest money long term which are a safer bet, but for those who love playing with fire, stock market is a good investment. One might ask why to invest in stocks than going for safer options. No other investment provides potentially higher profits and losses than stock market does. Investing in stock market is purely on people’s own risk. There is no such belief that a particular stock would always provide profit. Some people utilize the advancements of technology and computing resources in order to do algorithmic trading. One might say it’s a fool’s errand as there are some unfathomable factors which affect any stock. But could one gain an edge using these techniques? The proposed system explores this idea further by developing a Machine Learning model which accepts historic prices of stocks as input to predict futuristic prices with good accuracy to construct a portfolio of multiple stocks. The proposed project will help investors to gain an idea of whether investing in a stock may payout or not. © 2021 IEEE.