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2022-04-08 13:51:31 Open / Comment

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2022-04-06 09:03:31 Which models do you know for solving time series problems?

Simple Exponential Smoothing: approximate the time series with an exponentional function
Trend-Corrected Exponential

Smoothing (Holt‘s Method): exponential smoothing that also models the trend
Trend- and Seasonality-Corrected Exponential Smoothing

(Holt-Winter‘s Method): exponential smoothing that also models trend and seasonality

Time Series Decomposition: decomposed a time series into the four components trend, seasonal variation, cycling varation and irregular component

Autoregressive models: similar to multiple linear regression, except that the dependent variable y_t depends on its own previous values rather than other independent variables.

Deep learning approaches (RNN, LSTM, etc.) Open / Comment

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2022-04-04 19:52:00 How is time series different from the usual regression problem?

The principle behind causal forecasting is that the value that has to be predicted is dependant on the input features (causal factors). In time series forecasting, the to be predicted value is expected to follow a certain pattern over time. Open / Comment

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2022-04-02 10:38:37 Open / Comment

2022-04-01 10:09:11 Q. What is loss and cost function?

A. Loss function is a method of evaluating how well your algorithm models your data set. If your predictions are totally off, your loss function will output a higher number. If they’re pretty good, it’ll output a lower number. A loss function/error function is for a single training example/input. A cost function, on the other hand, is the average loss over the entire training dataset. Open / Comment

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