Abstract

One of information needed in weather forecast is air temperature. This value might change any time. Prediction of air temperature is very valuable for some communities and occasions. Therefore, high accuracy prediction is needed. Since the information about air temperature might vary over time, it is necessary to implement methods that can adapt to this situation. The use of neural network methods such as long short term memory (LSTM), nowadays, becomes popular in facing big data including unexpected fluctuation on the data. Thus, the model is used in this paper which provides long series data on air temperature. In addition, recently, Facebook announced an accurate method of forecasting, called Prophet model’s, for data which have trend, seasonality, holidays, missing data, not to mention outliers. Hence, the forecast of five-year daily air temperatures in Bandung on this paper is modeled by LSTM and Facebook Prophet. The result shows that, for minimum temperature, Prophet performs better on maximum air temperature while LSTM performs better on minimum air temperature. However, the difference on the value of RMSE is not too large significant.

摘要

天气预报所需的信息之一是气温。该值可能随时更改。气温的预测对于一些社区和场合是非常有价值的。因此，需要高精度的预测。由于有关气温的信息可能会随时间变化，因此有必要实施能够适应这种情况的方法。如今，使用神经网络方法（例如长短期记忆（LSTM））在处理大数据（包括数据的意外波动）时变得很流行。因此，本文使用的模型提供了关于气温的长系列数据。此外，最近，Facebook 宣布了一种称为 Prophet 模型的准确预测方法，适用于具有趋势、季节性、假期、缺失数据的数据，更不用说异常值了。因此，本文对万隆五年每日气温的预测是通过LSTM和Facebook Prophet建模的。结果表明，对于最低温度，Prophet 在最高气温方面表现更好，而 LSTM 在最低气温方面表现更好。然而，RMSE 值的差异不是太大显着。