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TIME SERIES - INTRODUCTION, CONCEPT, ANALYSIS (Excel, mini tab, SPSS)

TIME SERIES



INTRODUCTION

Time Series has primarily been a part of our everyday life, be it taking notes of daily sales, keeping data on the number of people infected by a particular ailment to Stock Data, Economic growth and GDP.

The Study of Time Series has helped us helped us Understand, Model and Forecast the future occurrences in the series, thereby preparing us and helping us develop precautionary measures when dealing with these topics.

In Summary, Time Series is data collected at a definite time interval, this could be data collected on a topic you want to study and the study of a Time Series is called Time Series Analysis 

"Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for"


Applications Of Time Series

Time series are used in statistics , signal processing , pattern recognition , econometrics,
mathematical finance , weather forecasting , intelligent transport and trajectory forecasting, earthquake prediction , electroencephalography, control engineering , astronomy , communications engineering , and largely in any domain of applied science and
engineering which involves temporal measurements, Economic Forecasting, Sales Forecasting, Budgetary Analysis, Stock Market Analysis, Yield Projections, Process and Quality Control, Inventory Studies, Workload Projections, Utility Studies, Census Analysis.


Basic Objectives Of Time Series Analysis

The basic objective usually is to determine a model that describes the pattern of the time series.

1. To describe the important features of the time series pattern.

2. To explain how the past affects the future or how two time series can “interact”.

3. To forecast future values of the series.


4. To possibly serve as a control standard for a variable that measures the quality of product in some manufacturing situations.


Characteristics to Consider First In A Time Series 

Before a series is modeled there are key things to consider, of which if any is ignored will lead to errors in both the model and Forecast

1. Is there a trend, meaning that, on average, the measurements tend to increase (or decrease) over time?

2. Is there seasonality, meaning that there is a regularly repeating pattern of highs and lows related to calendar time such as seasons, quarters, months, days of the week, and so on?
(seasonal Variations)

3. Are their outliers ? In regression, outliers are far away from your line. With time series data, your outliers are far away from your other data.

4. Is there a long-run cycle or period unrelated to seasonality factors? (Cyclical Variation)

5. Is there constant variance over time, or is the variance non-constant?

6. Are there any abrupt changes (Random And Irregular Variations)  to either the level of the series or the variance?

Before taking you deep into the basics and study of Time Series, I will like to give you a few definitions, you will come across while dealing with the topic


TIME SERIES Decomposition

Time series has two main forms of decomposition, these are the Multiplicative and additive decomposition

(i) The Additive Decomposition

Y(t) = Trend + Seasonal + Cyclical + Irregular

Y(t) = T(t) + S(t) + C(t) + I(t)

(ii) Multiplicative Decomposition

Y(t) = Trend * Seasonal * Cyclical * Irregular

Y(t) = T(t) * S(t) * C(t) * I(t)

Every time series model will fall in any of the two categories ( A Multiplicative or additive Model)


TOPICS

Topics might not look clickable due to the blogger template, just click on the top, it will take you to your required page

1. Time Series Forecasting

2. Component's Of Time SeriesTrend, Seasonal, Cyclical And Irregular Variations In Time Series

3. Stationarity In Time Series

4. Differencing In Time Series 

5. Auto-correlations And Partial Auto-correlations

6. Introduction To Moving Average

7. Introduction To Exponential Smoothing

7.1 Single Exponential Smoothing
7.2 Double Exponential Smoothing
7.3 Triple Exponential Smoothing

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