Time Series is a statistical method used to examine and project data points gathered throughout time. It is useful when working with data that displays temporal connections such as stock prices, weather patterns or dales data.
Time series modelling main objecctive is to understand the underlying patterns, trends, and correlations in the data and using the knowledge to forecast future values. This can be done by figuring out and modeling the different elements that make up a time series, like trend, seasonality, and noise.
Machine Learning models for time-series forecasting incluse Autoregressive conditional Heteroscedasticity(ARCH), Vector Autoregressive Model(VaR), LST and Prophet.
Here is the full guide to perform Time series modelling
1. Data Preprocessing
Gather and arrange your time series data, making sure it is in correct sequence.
Look for any concerns with data quality, such as missing numbers or outliers.
2. Exploratory Data Analysis (EDA)
visualize time series data to comprehend the patterns, trends, and seasonality.
3. Modelling
Choose an apropriate time series model depending on the properties of your data and the knowledge received from EDA.
Create training and testing sets of your data, making sure the testing set includes future time periods.
Using the right metrics, such as mean absolute error (MAE), mean squared error (MSE), or root mean square error (RMSE), fit the model on the training set and assess its performance on the testing set.
Evaluate the model's performance compared to baseline models or other alternative models.
If the model's performance is unsatisfactory, change the model's parameters, adding more features, or experimenting with new models entirely.
Repeat this procedure until you get an acceptable degree of precision and dependability.
Once you have a model that works well, you can use it to generate predictions for the future by adding fresh data points.
As fresh data becomes available, keep track of the model's performance over time and change it as necessary.
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