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What is a Time Series?
How to create a time series?
Visualizing a time series plot
How to convert a multiplicative time series into an additive time series
Logarithmic transformation its significance and its use
Assumptions of a time series forecasting model
To extract the trend, seasonality and random terms from the model
How to decompose a time series
Using standard regression-based methods to do forecast
How to identify autocorrelation and partial autocorrelation
How to de-trend a time series
Augmented Dickey-Fuller Test (ADF test for stationarity)
When and how to use the differencing of a time series
Double exponential smoothing model
MAPE metric to compare the accuracy of any time series model
What is the ARIMA model and steps to initialize model
Applying ARIMA model for forcasting
In this machine learning project, we will be taking open source datasets that are publicly available and will be discussing various methods/techniques of performing time series forecasting. We will discuss about the traditional methods such as holt-winters method, Autoregressive integrated moving average method, exponential smoothing methods, as well we will also be comparing the modern methods of performing forecasting using neural network based models.