Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 6
Time Series
An Exceptional Scenario
In the airline or hotel domain, the passenger load of month t is less
correlated with data of t-1 or t-2 month, but it is more correlated for t-12
month. For example, the passenger load in the month of Diwali (October)
is more correlated with last year’s Diwali data than with the same year’s
August and September data. Historically, the pick-up model is used to
predict this kind of data. The pick-up model has two variations.
In the additive pick-up model,
X(t) = X(t-1) + [X(t-12) –X(t-13)]
In the multiplicative pick-up model,
X(t) = X(t-1) * [X(t-12) / X(t-13)]
Studies have shown that for this kind of data the neural network–based
predictor gives more accuracy than the time-series model.
In high-frequency trading in investment banking, time-series models
are too time-consuming to capture the latest pattern of the instrument.
So, they on the fly calculate dX/dt and d2X/dt2, where X is the price of
the instruments. If both are positive, they blindly send an order to buy the
instrument. If both are negative, they blindly sell the instrument if they
have it in their portfolio. But if they have an opposite sign, then they do a
more detailed analysis using the time series data.
As I stated earlier, there are many scenarios in time-series analysis
where R is a better choice than Python. So, here is an example of timeseries
forecasting using R. The beauty of the auto.arima model is that it
automatically finds the order, trends, and seasonality of the data and fits
the model. In the forecast, we are printing only the mean value, but the
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