As we journey through the intricate landscape of time series modeling, it becomes apparent that our quest for understanding is evolving gradually. Let us embark on a comprehensive exploration of time series modeling, and I eagerly await your feedback.
Defining Time Series: A time series is a collection of data points meticulously recorded over time. It's a window into the past that offers us the opportunity to scrutinize the ever-shifting influences on specific variables across distinct time intervals.
Use & Importance of Time series analysis : Helps organizations understand the underlying causes of trends or systemic patterns over time. Using data visualizations, business users can see seasonal trends and dig deeper into why these trends occur. With modern analytics platforms, these visualizations can go far beyond line graphs.
Type of Time- Series models: There are various types of time-series forecasting models that help in predicting future values.
Time series models exhibit distinct characteristics:
Trend - T(t): a long-term upward or downward change in the average value.
Seasonality - S(t): a periodic change to the value that follows an identifiable pattern.
Residual R(t): random fluctuations in the time series data that does not follow any patterns.
Types of time-series models are:
- ARMA(Autoregressive + Moving Average): it is a combination of two parts - the model with p autoregressive terms and q moving-average terms. This model contains the AR(p) and MA(q) models.The combination of said models (ARMA) are linear models that work off of an assumption of a stationary input. Under this assumption, they can be used to predict a future occurrence based on previous observations if suitably defined.
- ARIMA(Autoregressive Integrated Moving Average) : It extends from ARMA model and incorporates the integrated component (inverse of differencing).Its used as a forecasting tool to predict how something will act in the future based on past performance. It is used in technical analysis to predict an asset's future performance.
- SARIMA(Seasonal Auto-Regressive Integrated Moving Average): An extension of the ARIMA which addresses the periodic pattern observed in the time series. They are specifically designed to handle data with seasonal patterns.
In conclusion, time series modeling equips us with the knowledge to unearth insights, make informed decisions, and forecast future trends. Whether you're a data scientist, a business analyst, or simply someone fascinated by the compelling world of data, this can help you with understanding what time series models are.
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