Beginners Guide on Time Series Forecasting.

Time Series Forecasting

Elements of Time Series

  1. Level — Level can be considered as the average or baseline value in the series.
  2. Seasonality — This is a very important factor that notes the repetitive patterns in time. For instance, a customer is likely to order more on Fridays. That is a 7-day repetitive pattern.
  3. Trend — Trend is the increase or decrease of values of the target variable with time. This often has a linear pattern which implies there is mostly a progressive increase or continuous decrease over time.
  4. Cycles — Irregular cyclic patterns can show up in the data that might not be bound to either seasonal boundaries or particular trends.
  5. Noise — Noise is the random variations in data over time that cannot be explained either through trends, seasonality, or any other pattern.

Key Concepts of Forecasting

Photo by Med Badr Chemmaoui on Unsplash
data['rolling_mean'] = data['net_amount'].rolling(window=3).mean()
data['lag'] = data['number_of_orders'].shift(7)

Use Cases of Time Series

Challenges in Time Series Forecasting

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