Develop a detailed time series forecasting model using ARIMA, including data preparation steps, model selection criteria, and evaluation metrics.
Task: generate a summary of a video. Input: [video title], [video transcript], [length of summary: e.g., 100 words, 200 words] Instruction: summarize the video transcript, focusing on the main topics and key takeaways. The summary should be concise and informative, providing a clear overview of the video's content. Adhere to the specified length constraint.
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Analyze time series data to identify trends, seasonality, and cyclical patterns, and recommend suitable forecasting models for future predictions.
Perform a systematic root cause analysis for identified data anomalies, pinpointing contributing factors and suggesting corrective actions to prevent recurrence.
Generate a detailed plan for feature engineering on time series data, including lag features, rolling statistics, and temporal indicators to enhance predictive models.
