Research breakdowns, practical implementation notes, and opinionated takes from real-world data and AI work.
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Time Series Analysis and Forecasting
Day 55 Objective of time seriesTime Series Chart (Excel)Time Series Chart (R) Day 56 Time Series componentsStationary TestQualitative Model vs Quantitative ModelTutorial…
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Day 65
Tutorial 5 Question 1 A volatile model is fitted to a stock return data and produce the following results (a) Write down…
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Day 64
Volatile Model (ARCH & GARCH) Technique to address irregular fluctuation that cannot be handle by typical time series model such as ARIMA,…
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Day 63
Tutorial 4 Question 1 The table below shows the result after fitting a time series data by an ARIMA model. (a) Write…
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Day 62
Box Jenkins Methodology (Recap) Split the below data into training (80%) and testing data (20%). Analyse the training data and formulate the model equation for the…
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Day 61
Box Jenkins Methodology A step by step process to identify and fitting ARIMA model hyperparameter; as the model itself has 3 main…
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Day 60
Measuring Predictive Accuracy Raw data (only for illustration) X Y Forecast Value 1 0.3324 1 2 2.9232 2 3 1.4348 3 4…
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Day 59
Recap. Differencing vs smoothing = differencing is about removing trend from time series, by “station” the data back to its mean; smoothing…
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Day 58
Smoothing Techniques (Con’t) Regression on seasonality and trend Linear Regression VS Trend Component Multi-Linear Regression VS / to handle Seasonality Component Using…
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Day 57
Smoothing Techniques Techniques Focus on Moving Average Stationary / Irregular Component Weighted Moving Average Exponential Moving Average Modified Moving Average Exponential Smoothing…
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Day 56
Time Series 4 Components that made up time series 1. Irregular fluctuations – Does not follow any available pattern and not predictable;…
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Day 55
Objective of time series Time Series Chart Excel 1. Raw Data Year Quarter 1 Quarter 2 Quarter 3 Quarter 4 1 667…
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