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You are a data scientist asked to analyze an avocado dataset by your team. The task at hand is to pick a specific avocado type in the whole of the US and forecast its daily average prices. To do that, you should train, apply, and score an ARIMA model. Do you see any seasonality in the line plot or autocorrelation plots? Do you think a seasonal ARIMA (SARIMA) would perform better? For your model, visualize forecasts and compute scoring metrics.
Level: MediumDescription: You are a data scientist asked to analyze an avocado dataset by your team. The task at hand is to pick a specific avocado typein the whole of the US and forecast its daily average prices. To do that, you should train, apply, and score an ARIMA model. Do you see anyseasonality in the line plot or autocorrelation plots? Do you think a seasonal ARIMA (SARIMA) would perform better? For your model,visualize forecasts and compute scoring metrics.あなたはアボカドのデータセットの分析をチームから依頼されたデータサイエンティストです。目の前の課題は、アメリカ全土から特定のアボカドの種類を選び、その1日の平均価格を予測することです。そのためには、ARIMAモデルを訓練し、適用し、スコアリングする必要があります。折れ線グラフや自己相関プロットに季節性が見えますか?季節性ARIMA (SARIMA)の方がうまくいくと思いますか?あなたのモデルについて、予測を可視化し、スコアリング指標を計算してください。 conventional: 18, 47, 52organic: 49load dataget Dateextract date & week infotypeNode 95type selectiontarget data extractiondateNode 125Node 127meanNode 136Node 137up: conventionaldown: organicconventionalup: conventionaldown: organicconventional_from_logarithmorganicorganic_from_logarithm Inspect Seasonality CSV Reader String to Date&Time Date&Time PartExtractor GroupBy Table Rowto Variable Single SelectionWidget Row Filter Sorter Math Formula Column Filter Date&TimeAggregator Column Filter Rule EngineVariable CASE Switch Start SARIMA_conventional CASE Switch Start SARIMA_conventional SARIMA_organic SARIMA_organic ARIMA Level: MediumDescription: You are a data scientist asked to analyze an avocado dataset by your team. The task at hand is to pick a specific avocado typein the whole of the US and forecast its daily average prices. To do that, you should train, apply, and score an ARIMA model. Do you see anyseasonality in the line plot or autocorrelation plots? Do you think a seasonal ARIMA (SARIMA) would perform better? For your model,visualize forecasts and compute scoring metrics.あなたはアボカドのデータセットの分析をチームから依頼されたデータサイエンティストです。目の前の課題は、アメリカ全土から特定のアボカドの種類を選び、その1日の平均価格を予測することです。そのためには、ARIMAモデルを訓練し、適用し、スコアリングする必要があります。折れ線グラフや自己相関プロットに季節性が見えますか?季節性ARIMA (SARIMA)の方がうまくいくと思いますか?あなたのモデルについて、予測を可視化し、スコアリング指標を計算してください。 conventional: 18, 47, 52organic: 49load dataget Dateextract date & week infotypeNode 95type selectiontarget data extractiondateNode 125Node 127meanNode 136Node 137up: conventionaldown: organicconventionalup: conventionaldown: organicconventional_from_logarithmorganicorganic_from_logarithm Inspect Seasonality CSV Reader String to Date&Time Date&Time PartExtractor GroupBy Table Rowto Variable Single SelectionWidget Row Filter Sorter Math Formula Column Filter Date&TimeAggregator Column Filter Rule EngineVariable CASE Switch Start SARIMA_conventional CASE Switch Start SARIMA_conventional SARIMA_organic SARIMA_organic ARIMA

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