(for simple exercises) Time Series Forecasting

**Alcohol_Sales.csv**: This dataset was taken from https://fred.stlouisfed.org/series/S4248SM144NCEN(old url https://fred.stlouisfed.org/series/.)

**energydata_complete.csv**: Experimental data used to create regression models of appliances energy use in a low energy building. Data Set Information:
The data set is at 10 min for about 4.5 months. The house temperature and humidity conditions were monitored with a ZigBee wireless sensor network. Each wireless node transmitted the temperature and humidity conditions around 3.3 min. Then, the wireless data was averaged for 10 minutes periods. The energy data was logged every 10 minutes with m-bus energy meters. Weather from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis (rp5.ru), and merged together with the experimental data sets using the date and time column. Two random variables have been included in the data set for testing the regression models and to filter out non predictive attributes (parameters). The original source of the dataset:
http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction

Alcohol_Sales.csv：本数据集源自https://fred.stlouisfed.org/series/S4248SM144NCEN（旧链接：https://fred.stlouisfed.org/series/.)

energydata_complete.csv：用于构建低能耗建筑中家用电器能源使用回归模型的实验数据集。数据集信息：该数据集涵盖了大约4.5个月的10分钟数据。通过ZigBee无线传感器网络监测了房屋的温湿度条件。每个无线节点大约每3.3分钟传输一次周围的温湿度条件。然后，将无线数据平均到10分钟周期。能源数据使用m-bus能源表每10分钟记录一次。从最近的机场气象站（比利时切夫尔机场）下载的天气数据来自可靠的预报（rp5.ru）的公共数据集，并与实验数据集合并，使用日期和时间列进行合并。数据集中包含两个随机变量，用于测试回归模型并筛选出非预测性属性（参数）。数据集的原始来源：http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction