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Time Series Analysis Time Series analysis, a part of predictive analysis is used predicting future data based on past data. It is the analysis of a sequence of data points over a consistent interval of time. The data is not recorded at random but in regular intervals. For accurate results, it requires a larger data set in order for the data to be representative, ensure reliability and consistency. The analysis depicts the change of the variable over time. Statistical software with good data visualization capability helps in the analysis and visualization and prediction of the time series. Time series analysis is used in place where the data is affected by time such as retail, stock markets, foreign exchange markets, cryptocurrency, medical data, weather data, educational data and many other areas. Classification of Time Series Analysis. Time series analysis can be classified as trend analysis, seasonal variation or cyclical variation. Trend Analysis can be deterministic or stochastic. In deterministic trend analysis, the underlying cause can be explained, however in stochastic trend analysis, the underlying cause in random and is unexplained. A trend may increase, decrease or move sideways over a period of time. Seasonal Variation generally takes place at regular intervals during the year. It occurs at a specific and regular time interval. It occurs due to the rhythmic forces that occur in a periodic manner such as the revolution of the earth around the sun or the moon around the earth. Seasonal variations repeat themselves over time. Seasonal variations occur due to habits, traditions, festivals, weather etc. Example of seasonal variation is Air Fares, hotel bookings, sale of winter clothing, etc. Cyclical variation occurs due to various factors. It differs from the seasonal variation in that the variation follows an irregular periodic pattern. It could be due to the business cycle such as boom, recession, depression and recovery. In a cyclical variation the outcomes may differ between each cycle. Example of Cyclical variations are the savings of a person during various stages of his life. These savings are different for each individual, hence more difficult to precicr Application of Time Series. It is used on predicting the future based on past data. It us used to detect the underlying cause, seasonal, cyclical trends or systemic patterns that occur over a period of time. It is used to detect seasonal variations that occur due to habits, traditions, festivals, weather etc. It is also used to detect cyclical variation such as business cycles, etc. Most Difficult to Handle The cyclic component is longer and less predictable when compared to the seasonal component which is shorter and easier to predict. Some cyclic components such as business cycles, could go on for a few years or decades. References https://www.tableau.com/learn/articles/time-series-analysis https://www.toppr.com/guides/fundamentals-of-business-mathematics-and-statistics/time-series-analysis/definition-of-time-series-analysis/ https://www.coursebb.com/2017/07/24/difference-cyclical-component-seasonal-component/

Time Series data is very common in most of the projects in Business Excellence. There are lot of analytics performed using this data. The most common is Time Series Regression. This helps us to understand and predict the behavior of the data. Most of the organization uses time series data to understand the underlining causes of trends. They also use data visualization tools to analyze the seasonal trends and to go in depth to understand the reason behind the trends. We can use this technique to Forecast · Volume · Customer Satisfaction · Staff Turnover · Online hits, and so on Any time series can contain some or all the following components: Type Description Example Trend Describes movement along the term. Describes the pattern with long term increase or decrease in data Mathematical finance, weather forecasting, earthquake prediction, and so on Cyclical Describes Seasonal changes or pattern due to the calendar Sale of a commodity, Height of tides, etc. Seasonal Describe Periodical but not seasonal. The data exhibits rise and falls that are not of fixed period. Temperature, Rainfall, etc In most of the cases both seasonal and cyclic looks similar. Let us understand the difference. Seasonal Cyclic Constant Length Variable Length Average length is shorter Average length is longer Magnitude is less Magnitude is more Since the peaks and troughs are not predictable in Cyclic, this time series is difficult to use.

Out of Trend, Cyclical and Seasonal components of time series, Cyclical variations is the most difficult to handle because usually the time period considered is for 3 to 8 years and in today's times, it is difficult to understand the regularity and variations. The rate of disruption is very high because of which it is difficult to rely on past trend (always) and predict how the respective industry will be impacted and to what extend Below are some of the applications of time series 1. Volume and time study 2. Headcount, sales, revenue forecasting 3. Risk forecasting

The 3 main components of a time-series data are: Trend – Where the data values exhibit either increased or decreased pattern in a reasonably predictable manner. Seasonal – Where the data values follow a repeated pattern over a specific period such as the time of the year or the day of the week. Seasonality is always of a fixed and known frequency. Cyclical – Where the data values exhibit rise and fall but not of a fixed frequency. The pattern is usually influenced by the economic condition. · The analysis of trend is not difficult if the trend just follows a continuously increasing or decreasing pattern. We just need to find a right model to describes the trend and subtract it trend from the analysis. · Seasonality is also like a trend i.e. if we examine carefully, seasonality is nothing but a variable trend. Instead of increasing or decreasing at a predictable manner, it increases or decreases with pattern that vary with time. Analyzing a seasonality is also similar to trend, where we must find a right model and subtract it. However, it is little difficult to find the right model when compared to trend. · Analysis of cyclical data is the most difficult as there is no fixed time frequency in the data pattern. Both the average length & magnitude of cycles is longer than that of seasonality. Hence, finding the right model to describe become the most difficult. Time Series Analysis can be applicable in Finance sector particularly for Stock Prediction, country’s GDP prediction, etc. In the Retail sector it can be used for Sales Forecasting. From healthcare sector perspective, Time Series Analysis can be used in inventory or material demand prediction. One of the best examples is predicting the vaccine stock for immunization which will help in determining the vaccine stock requirements for clinics to avoid shortage or excess vaccines in stock especially during the peak disease season.

A time series is a sequence of data in accordance with occurrence over time. It is a uniformly spaced observations over the period of time. The data is formed by collecting data over a long range of time and can be hours, days or months. Time series represents the relationship between two variables – 1) time and 2) any quantitative variable. Time series helps to predict future behaviour of variable basis past experience. Hence, it helps in studying the past behaviour of the variable. The time series components are – · Trend – It is used to see the trend going upward, downward or constant basis the slope of the trend line. · Seasonal – This can be easily highlighted in the time series graph. It shows the peaks and lows of a variable at a regular interval · Cyclic Variations – It occurs as a change in qualitative nature of the variable. It is difficult to detect from a graph and can be missed in a short term data.

Time-series analysis is a technique for analysing time series data which is a series of data points recorded over a specified period. The analysis extracts meaningful statistical information and helps to forecast future value. It also helps to identify the characteristics of the data. Time series(Y) have following components: 1. Long term trend(T) – It is the movement of any data where in the short-term effects such as seasonal variations or cyclic variations are ignored. For example, the enrolment trend in a university may be a steady climb on average over the past 50 years. This trend will be visible despite having a few years of loss or stagnant enrolment followed by years of rapid growth. 2. Cyclical effect(C) - This has relatively long-term patterns of oscillation in the data. These cycles may take many years. For example- There are various long cycles in business economics which take lot of years. 3. Seasonal effect(S) – Patterns of ups and downs which can be predicted and occurs at repeatable intervals year on year. For example – Weather forecast shows seasonal variations in temperature. 4. Error effect(N) - Every set of data has errors. These are random variations and occurs due to factors which cannot be controlled. The time series(Y) consists of the product of the individual factors and is represented as below: Time Series = Long Term Trend X Cyclical Effect X Seasonal Effect X Error Effect Y= T x C x S x N

Time Series Analysis Time series is an ordered sequence of values that a variable takes over time that could either be having regular or irregular pattern. Time Series Analysis(TSA) enables us to understand how predictions are made based on trend, seasonal and cyclical variations. Types of Time Series 1. Trend: It is the easiest of Time series which helps fit our data to a predetermined model and make future predictions. 2. Seasonal: The seasonal time series helps us to understand the season driven variations that recurs invariably during the same period every calendar year. The classic examples is sales during festive season every year and seasonal farming. 3. Cyclical: Cyclical Time series is a characteristic of a time series that can span shorter or longer than one calendar year. The classic example economic trends and stock market variations are cyclical. This is perhaps the most difficult to handle among all time series due to uncertainty. TSA Applications TSA is used for both industrial and non-industrial applications in the business excellence world.Some of the most common areas of it being extensively used are: · Economic Forecasting · Budget Analysis · Inventory Studies · Stock Market Analysis · Utility Studies · Census Analysis · Yield Projections · Sales Forecasting · Workload projections

Introduction to TSA: Time Series analysis is the method of studying the characteristics of the response variable in respect to the time as the independent value. To evaluate the target variable in the name of prediction or forecasting, use the time variable as the point of reference. Components of TSA: Trend: There is no fixed interval and disagreement within the given dataset is a continuous timeline. The trend might be Negative, Positive or Neutral Seasonality: There are regular or fixed interval shifts within the dataset in a continuous timeline. Would be bell curve type Cyclical: There is no fixed interval, uncertainty in movement and its pattern As per my understanding Cyclical is the most difficult component to handle as it has no fixed interval and uncertain movements and pattern. Some applications of time Series Analysis used in the Business Excellence world are mentioned below: Time series analysis can be used in Business forecasting, Business development as Time series forecasting helps businesses to make informed business decisions, as the process analyses past data patterns it can be useful in forecasting future possibilities.

3 Types of Time Series Analysis 1. Trend - It shows the general tendency of the data are increased, decreased or stagnate in a reasonably predictable pattern and / or long period of time. An application of this is when we analyze the KPI Performance of a team over a week, month, quarter or year. As these type of data in a report generally show trends or show patterns over a period of time. 2. Cyclical - The values of the data exhibit ups and downs repeating after a period from time to time due to economic or business cycle. An application of this is when we analyze business growth or recessions, depressions and recovery. 3. Seasonal - It has an indicative in which the data arrangement or experiences regular, foreseeable and repeated every calendar year. An application of this is when we analyze variations due seasons, weather, traditions etc. Overall the most difficult to handle type of Time Series is Seasonal as when you need to improve a process with this type of data, you have limited data points and sometimes does not show trends or patterns.