燕窝果批发价格预测分析数据

下游零售商、水果连锁店可以根据燕窝果价格预测趋势，在价格低点时增加采购量，在高点时减少采购量，从而显著降低采购成本。同时结合销售预测，企业可以制定更优的库存策略。例如，当预测价格即将上涨时，可适当增加安全库存；当预测价格下跌时，则加速出货、减少库存积压，实现“精益库存”。另外，短期价格预测有助于物流公司预判市场活跃度，合理调配冷链运输资源，优化仓储布局。1.数据采集：企业以“国研掌上果贸结算通平台”的真实交易数据为基础，汇总并提取出预测分析日前7日的燕窝果批发价格历史数据，从而构建出时间序列数据集。 2. 数据预处理：对数据进行清洗，去除缺失值和异常数据。3. 数据分析： 使用加权移动平均法。T= （T-1*7+T-2*6+T-3*5+T-4*4+T-5*3+T-6*2+T-7*1）/7+6+5+4+3+2+1 T:T日建议批发价格即预测分析日当日建议批发价格（件/元)； T-1：预测分析日前一日批发价格（件/元)； T-2: 预测分析日前二日批发价格（件/元)…… T-7：预测分析日前七日批发价格（件/元)。

Downstream retailers and fruit retail chains can leverage yellow pitaya price forecasting trends to increase procurement volumes at price troughs and reduce procurement volumes at price peaks, thereby significantly lowering overall procurement costs. Combined with sales forecasting, enterprises can develop more optimized inventory strategies. For example, when prices are forecast to rise, appropriate safety stock levels can be increased; when prices are expected to fall, shipments can be accelerated and inventory backlog reduced to achieve "lean inventory" practices. Additionally, short-term price forecasting helps logistics companies anticipate market activity, rationally allocate cold chain transportation resources, and optimize warehouse layout.

1. Data Collection: Enterprises construct a time-series dataset by summarizing and extracting historical wholesale price data of yellow pitaya covering the 7 days prior to the forecast analysis date, using real transaction data sourced from the "Guoyan Palm Fruit Trade Settlement Platform".
2. Data Preprocessing: Data cleaning is conducted to eliminate missing values and abnormal data entries.
3. Data Analysis: The weighted moving average method is employed. The calculation formula is:
T = (T_{-1}×7 + T_{-2}×6 + T_{-3}×5 + T_{-4}×4 + T_{-5}×3 + T_{-6}×2 + T_{-7}×1) / (7 + 6 + 5 + 4 + 3 + 2 + 1)
Where:
- T: Recommended wholesale price on day T, i.e., the suggested wholesale price for the forecast analysis date (unit: yuan per piece);
- T_{-1}: Wholesale price on the day immediately preceding the forecast analysis date (unit: yuan per piece);
- T_{-2}: Wholesale price on the second day preceding the forecast analysis date (unit: yuan per piece);
- ……
- T_{-7}: Wholesale price on the seventh day preceding the forecast analysis date (unit: yuan per piece).

搜集汇总

背景与挑战

背景概述

该数据集由国研软件股份有限公司提供，包含615条燕窝果批发价格历史记录和预测数据，每年更新一次，用于基于加权移动平均法的价格趋势分析。其应用场景包括帮助零售商和物流公司优化采购、库存和资源配置，以降低成本和提升效率。