(A) study on forecasting gold price in the short run based on artificial neural network

다국어 초록 (Multilingual Abstract)

Forecasting time series data in financial markets has been one of the most interesting topics since people had a natural desire on earning profits. Because there are no absolute rules or theories that explain movements in any time series data, it seems to be impossible to predict a value in the future. However, some methods have been introduced in statistics and currently data science contributes to forecasting the future values in not only financial markets in economics, but also other time series data such as signals, weather patterns, and people’s demand in manufacture markets, etc.
Forecasting the future gold price in the financial markets is so important that it supports financial investors to read and analyze signals from economic indicators and make decisions on their assets. Mostly, Autoregressive Integrated Moving Average (ARIMA) and Multilayer Perceptron (MLP) have been used in gold market in various studies. On the other hand, several deep learning algorithms from Convolution Neural Network (CNN) to Long Short-term Memory (LSTM) are already used in especially stock markets and cryptocurrency markets meaning that time series forecasting in financial markets may be more accurate with state-of-the-art deep learning algorithms.
In this thesis, we focus on typically the gold market which investors consider it as a safe-haven asset and we propose a method that is a combination of CNN, LSTM and MLP to predict the next 5-day gold price. First, we use a hybrid CNNLSTM model to predict the target values and add residuals from the previous prediction as another input to predict the next 5-day gold price with MLP. We discover that the results from our proposed method are better than other wellknown methods such as Linear Regression, ARIMA, and Artificial Neural Network (ANN).

목차 (Table of Contents)

• 1. Introduction 1
• 2. Related Works 4
• 2.1 Gold Price Prediction 4
• 2.1.1 Statistical Analysis 4
• 2.1.2 Univariate vs. Multivariate 5
• 2.2 Multi-step Ahead Time Series Forecasting Technique 6
• 2.2.1 Direct Strategy 6
• 2.2.2 Recursive Strategy 7
• 2.2.3 MIMO Strategy 7
• 2.3 Deep Learning Algorithms 9
• 2.3.1 MLP 9
• 2.3.2 CNN 11
• 2.3.3 LSTM 14
• 3. Our Proposed Model 16
• 3.1 1D CNN-LSTM 16
• 3.2 Residual Correction 18
• 4. Experiment 21
• 4.1 Data Collection 21
• 4.2 Data Preprocessing 25
• 4.3 Training 29
• 4.3.1 ARIMA 29
• 4.3.2 Neural Networks training 30
• 5. Experiment Result 32
• 5.1 Evaluation Methods 32
• 5.2 Performance Evaluation 34
• 6. Conclusion 45