# 副次評価項目（ドリルの突出長）の表 # 基準値を10mmに変更

Time-series analysis and forecasting of zinc prices (1990-2025) using ARIMA models in R. The project includes data preprocessing, stationarity diagnostics (ADF, Box-Cox), autocorrelation analysis, model selection using AIC/BIC, and out-of-sample forecast evaluation with comparison to Holt exponential smoothing benchmarks.

1. Findings and Conclusions After processing the raw data and using the ARIMA package to model ride-share data, I was able to make predictions for the 25 days beyond the current data set. 2. Qualitatively the data shows that as the weather gets warmer, the number of bike rentals increases, and over the course of two years, the number of rentals increases over the number of rentals from the previous year. 3. As the data terminates at the end of one cycle, I expect the number of rentals to increase to a level higher than it was a year before, which is what the models are predicting. 4. Therefore, the results were what I expected; the data appears to oscillate up and down over a 1-year period, with the overall data moving towards higher rental numbers.

stats final