APPLICATION OF FUZZY TIME SERIES WITH FIBONACCI RETRACEMENT FOR FORECASTING STOCK PRICE PT. BANK RAKYAT INDONESIA

Stock can be defined as securities that indicate the ownership of a person or legal entity to the company issuing the shares. Good stocks for long-term investment are stocks that have good fundamentals and large market capitalization. The purpose of investing is to make a profit. In investing in stocks, investors need to know the risk management that can affect the ups and downs of a stock. Forecasting or forecasting is an analysis to predict everything related to the production, supply, demand, and use of technology in an industry or business. One of the forecasting methods is using fuzzy time series. The primary purpose of fuzzy time series is to predict time series data that can widely use on any real-time data, including capital market data. In this study, we will discuss the evolution of the time series model in overcoming fluctuations that often occur in stock prices by using a fuzzy time series that combines a stock analysis approach, namely Fibonacci retracement. The stock data used in this study is the close price of BBRI for October 2021 to March 2022. Forecasting results for 1 April 2022 are IDR 4660.49 with a Mean Absolute Percentage forecasting accuracy value of 1.034%

Ratio Interval-Frequency Density with Modifications to the Weighted Fuzzy Time Series

The improvement of plantation forecasting accuracy, particularly with regard to coffee production, was an essential aspect of earth observations for the purpose of informing plantation management alternatives. These decisions included strategic and tactical decisions on supply chain operations and financial decisions. Many research initiatives have used a variety of methodologies to the forecasting of plantation areas and related industries, such as coffee production. One of these methods was known as the fuzzy time series (FTS) technique. This  study combined ratio-interval and frequency density to get universe of discourse and partition followed by adopted weighted and modified that weighted. The first step was defined universe of discourse using ratio-interval algorithm. The second step was partition the universe of discourse using ratio-interval algorithm followed by frequency density partitioning. The third step was fuzzyfication. The fourth step built fuzzy logic relationship (FLR) and fuzzy logic relationship group (FLRG). The fifth step was adopted the modification weighted. The last step was defuzzyfication. The  models evaluated  by  average  forecasting  error  rate  (AFER)  and  compared  with  existing methods.  AFER  value  1.24%  for  proposed method

Research of fuzzy time series model based on fuzzy entropy and fuzzy clustering

时间序列预测是通过对有限个历史观测样本进行分析来建立模型，并利用模型来解释数据之间的统计规律，以期达到控制和预报目的的一门学科，在众多领域中都有非常广泛的应用。对于时间序列的建模和预测，目前已经有了许多成熟的技术和方法，但传统时间序列预测方法往往依赖大量的历史数据，而在实际问题中由于不确定性的广泛存在导致历史数据往往是不完整的、不准确的和含糊的，因而限制了传统预测模型的应用。为了解决这些问题，Song和Chissom提出了模糊时间序列的概念，其主要是在传统时间序列预测的基础上引入了模糊理论，通过建立相应的模糊逻辑关系进行预测。由于模糊时间序列在处理数据的不确定性和模糊性方面上所显示的优势，关于...Time series forecasting is modeled by limited historical observations sample, it is a technology of using the model to explain the statistical regularity of data in order to achieve the purpose of control and forecast and having a wide range of applications in many fields. For time series modeling and forecasting, there are many mature technologies and methods. The traditional time series predicti...学位：理学硕士院系专业：数学科学学院_概率论与数理统计学号：1902010115250

Application of Machine Learning to Financial Time Series Analysis

This multidisciplinary thesis investigates the application of machine learning to financial time series analysis. The research is motivated by the following thesis question: ‘Can one improve upon the state of the art in financial time series analysis through the application of machine learning?’ The work is split according to the following time series trichotomy: 1) characterization — determine the fundamental properties of the time series; 2) modelling — find a description that accurately captures features of the long-term behaviour of the system; and 3) forecasting — accurately predict the short-term evolution of the system