A Time Series Analysis to Forecast Price Fluctuation

Nowadays, price fluctuation point forecast is usually relying on the human judgments, and cause many opportunities of saving cost missed. For a company, buying material at a lower price and selling products at a higher price are the straightest way to obtain higher revenue. If there is a way to predict the price fluctuation of material or products accurately, a company can maximize its profit by taking a right action at a right time. This study introduces a novel forecast procedure for price fluctuation points forecast. This study proposes a price fluctuation forecast model: Price Fluctuation Point Forecast Approach (PFPFA). We not only forecast the price change degree, but also the price change time. Since the transaction data are non-uniform sampled time series, we will use quantity to present time to solve this problem. The main process of PFPFA has four phases: (1) transforming data based on the number of fluctuation points; (2) calculating times with different forecast models; (3) calculating prices based on the results of P2 with different forecast models; and (4) evaluating and selecting the best forecast model combination for groups. In this paper, we propose four models for time forecast and three models for price forecast. In consequence, for a single product, there would be twelve different forecast outcomes. we applied PFPFA in a real world case, and compare the result with the Exponential Smoothing (ES) which is commonly and currently used. The time forecast result is acceptable and the price forecast result shows that PFPFA has better performance than ES.