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Time Series Analysis - 1 | Time Series in Excel | Time Series Forecasting | Data Science|Simplilearn

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This Time Series Analysis (Part-1) tutorial will help you understand what is time series, why time series, components of time series, when not to use time series, why does a time series have to be stationary, how to make a time series stationary and at the end, you will also see a use case where we will forecast car sales for 5th year using the given data.
Link to Time Series Analysis Part-2: https://www.youtube.com/watch?v=Y5T3ZEMZZKs
You can also go through the slides here: https://goo.gl/RsAEB8
A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this video and understand what is time series and how to implement time series using R.
Below topics are explained in this Time Series in R Tutorial -
1. Why time series?
2. What is time series?
3. Components of a time series
4. When not to use time series?
5. Why does a time series have to be stationary?
6. How to make a time series stationary?
7. Example: Forecast car sales for the 5th year
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{'title': 'Time Series Analysis - 1 | Time Series in Excel | Time Series Forecasting | Data Science|Simplilearn', 'heatmap': [{'end': 1362.306, 'start': 1296.077, 'weight': 0.839}, {'end': 1477.292, 'start': 1451.128, 'weight': 0.783}, {'end': 1805.434, 'start': 1747.297, 'weight': 0.818}], 'summary': 'Series on time series analysis and forecasting in excel covers the fundamentals, components, and graphical representation of time series data, addressing seasonal increase in warm clothes sales, limitations of time series analysis, and the use of regression tool in excel for data analysis and prediction.', 'chapters': [{'end': 83.787, 'segs': [{'end': 83.787, 'src': 'embed', 'start': 33.157, 'weight': 0, 'content': [{'end': 41.785, 'text': "and then we'll talk about what exactly is time series data and then what are the components of a time series data like, for example, the trend,", 'start': 33.157, 'duration': 8.628}, {'end': 46.489, 'text': 'seasonality and so on, and then we will explain what each of these components are.', 'start': 41.785, 'duration': 4.704}, {'end': 59.376, 'text': "we'll also talk about when we should not use time series analysis and we will then explain what is stationary data in terms of time series.", 'start': 47.009, 'duration': 12.367}, {'end': 62.398, 'text': 'what is the significance of stationary data?', 'start': 59.376, 'duration': 3.022}, {'end': 65.44, 'text': 'and typically time series data is not stationary.', 'start': 62.398, 'duration': 3.042}, {'end': 68.562, 'text': 'so how to make time series data stationary?', 'start': 65.44, 'duration': 3.122}, {'end': 83.787, 'text': 'and then we will take you through an example of a car sales data and we will show you how to solve the time series or how to perform the time series analysis manually without using a tool,', 'start': 69.382, 'duration': 14.405}], 'summary': 'Exploring time series data components, significance of stationary data, and manual time series analysis', 'duration': 50.63, 'max_score': 33.157, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf833157.jpg'}], 'start': 3.424, 'title': 'Time series forecasting', 'summary': 'Introduces the fundamentals of time series forecasting, covering the significance of time series analysis, components of time series data, and the process of making time series data stationary. it demonstrates manual time series analysis using car sales data.', 'chapters': [{'end': 83.787, 'start': 3.424, 'title': 'Introduction to time series forecasting', 'summary': 'Covers the fundamentals of time series forecasting, including the significance of time series analysis, components of time series data, and the process of making time series data stationary, with an example of car sales data being used to demonstrate manual time series analysis.', 'duration': 80.363, 'highlights': ['The chapter covers the significance of time series analysis, components of time series data, and the process of making time series data stationary.', 'Explains what time series data is and its components such as trend and seasonality.', 'Discusses the significance of stationary data in time series analysis and how to make time series data stationary.', 'Provides an example of car sales data to demonstrate manual time series analysis without using a tool.']}], 'duration': 80.363, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf83424.jpg', 'highlights': ['The chapter covers the significance of time series analysis, components of time series data, and the process of making time series data stationary.', 'Explains what time series data is and its components such as trend and seasonality.', 'Discusses the significance of stationary data in time series analysis and how to make time series data stationary.', 'Provides an example of car sales data to demonstrate manual time series analysis without using a tool.']}, {'end': 375.982, 'segs': [{'end': 166.68, 'src': 'embed', 'start': 138.525, 'weight': 0, 'content': [{'end': 141.746, 'text': 'so we have some past data and we want to predict the future.', 'start': 138.525, 'duration': 3.221}, {'end': 144.327, 'text': 'that is when we perform time series analysis.', 'start': 141.746, 'duration': 2.581}, {'end': 146.628, 'text': 'what are some of the examples?', 'start': 144.847, 'duration': 1.781}, {'end': 155.994, 'text': 'it could be daily stock price, the shares, as we talk about, or it could be the interest rates, weekly interest rates or sales figures of a company.', 'start': 146.628, 'duration': 9.366}, {'end': 159.856, 'text': 'so these are some of the examples where we use time series data.', 'start': 155.994, 'duration': 3.862}, {'end': 166.68, 'text': 'we have historical data which is dependent on time and then, based on that, we create a model to predict the future.', 'start': 159.856, 'duration': 6.824}], 'summary': 'Time series analysis uses historical data like daily stock prices, interest rates, and sales figures to predict the future.', 'duration': 28.155, 'max_score': 138.525, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8138525.jpg'}, {'end': 254.444, 'src': 'embed', 'start': 211.204, 'weight': 1, 'content': [{'end': 214.627, 'text': 'It could be every few milliseconds or microseconds as well.', 'start': 211.204, 'duration': 3.423}, {'end': 219.009, 'text': 'So the size of the time intervals can vary, but they are fixed.', 'start': 214.827, 'duration': 4.182}, {'end': 225.052, 'text': "So if I'm saying that it is daily data, then the interval is fixed as daily.", 'start': 219.189, 'duration': 5.863}, {'end': 231.015, 'text': "If I'm saying this data is an hourly data, then it is the data is captured every hour and so on.", 'start': 225.353, 'duration': 5.662}, {'end': 232.656, 'text': 'So the time intervals are fixed.', 'start': 231.096, 'duration': 1.56}, {'end': 237.659, 'text': 'The interval itself, you can decide based on what kind of data we are capturing.', 'start': 233.017, 'duration': 4.642}, {'end': 240.42, 'text': 'so this is a graphical representation, the previous one.', 'start': 237.819, 'duration': 2.601}, {'end': 244.621, 'text': 'here we saw the table representation and this is how to plot the data.', 'start': 240.42, 'duration': 4.201}, {'end': 250.483, 'text': "so on, the y-axis is, let's say, the price or the stock price, and x-axis is the time.", 'start': 244.621, 'duration': 5.862}, {'end': 254.444, 'text': 'so against time, if you plot it, this is how a time series graph would look.', 'start': 250.483, 'duration': 3.961}], 'summary': 'Time series data has fixed intervals, e.g. daily or hourly, and can be graphically represented with time on x-axis and price on y-axis.', 'duration': 43.24, 'max_score': 211.204, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8211204.jpg'}, {'end': 332.138, 'src': 'embed', 'start': 277.552, 'weight': 2, 'content': [{'end': 280.073, 'text': 'so time is one of the components of this data.', 'start': 277.552, 'duration': 2.521}, {'end': 284.195, 'text': 'Time series data consists of primarily four components.', 'start': 280.273, 'duration': 3.922}, {'end': 293.641, 'text': 'One is the trend, then we have the seasonality, then cyclicity, and then last but not least, irregularity or the random component.', 'start': 284.856, 'duration': 8.785}, {'end': 296.563, 'text': "Sometimes it's also referred to as a random component.", 'start': 293.681, 'duration': 2.882}, {'end': 299.986, 'text': "so let's see what each of these components are.", 'start': 296.903, 'duration': 3.083}, {'end': 300.927, 'text': 'so what is trend?', 'start': 299.986, 'duration': 0.941}, {'end': 308.313, 'text': 'trend is overall change or the pattern of the data, which means that the data may be.', 'start': 300.927, 'duration': 7.386}, {'end': 311.636, 'text': 'let me just pull up a pen and show you so.', 'start': 308.313, 'duration': 3.323}, {'end': 318.342, 'text': "let's say you have a data set somewhat like this, a time series data set somewhat like this, all right.", 'start': 311.636, 'duration': 6.706}, {'end': 323.773, 'text': 'So what is the overall trend?', 'start': 321.371, 'duration': 2.402}, {'end': 329.377, 'text': 'There is an overall trend which is upward trend, as we call it here right?', 'start': 323.813, 'duration': 5.564}, {'end': 330.677, 'text': 'So it is not like.', 'start': 329.717, 'duration': 0.96}, {'end': 332.138, 'text': 'it is continuously increasing.', 'start': 330.677, 'duration': 1.461}], 'summary': 'Time series data consists of trend, seasonality, cyclicity, and irregularity/random component.', 'duration': 54.586, 'max_score': 277.552, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8277552.jpg'}, {'end': 380.307, 'src': 'embed', 'start': 352.134, 'weight': 6, 'content': [{'end': 355.859, 'text': 'so, for example, here there is a downward trend, right.', 'start': 352.134, 'duration': 3.725}, {'end': 363.187, 'text': 'so this is basically what is a trend overall, whether the data is increasing or decreasing, All right.', 'start': 355.859, 'duration': 7.328}, {'end': 365.911, 'text': 'Then we have the next component, which is seasonality.', 'start': 363.227, 'duration': 2.684}, {'end': 373.679, 'text': 'What is seasonality? Seasonality, as the name suggests, once again, changes over a period of time and periodic changes.', 'start': 365.971, 'duration': 7.708}, {'end': 375.982, 'text': 'Right So there is a certain pattern.', 'start': 374.24, 'duration': 1.742}, {'end': 380.307, 'text': "Let's take the sales of warm clothes, for example.", 'start': 376.943, 'duration': 3.364}], 'summary': 'Analyzing data trends and seasonality to identify patterns and make predictions.', 'duration': 28.173, 'max_score': 352.134, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8352134.jpg'}], 'start': 83.787, 'title': 'Time series analysis', 'summary': 'Introduces the concept of time series analysis, emphasizing its purpose to predict future events based on historical data such as stock prices, sales figures, and interest rates, and discusses the components and graphical representation of time series data.', 'chapters': [{'end': 232.656, 'start': 83.787, 'title': 'Introduction to time series analysis', 'summary': 'Introduces the concept of time series analysis, explaining its purpose to predict future events based on historical data such as stock prices, sales figures, and interest rates, emphasizing the significance of time as a crucial component and the fixed intervals in time series data.', 'duration': 148.869, 'highlights': ['Time series analysis is used to predict future events based on historical data such as stock prices and sales figures.', 'Time series data has time as a crucial component, and the time intervals are fixed, varying from daily to hourly or even milliseconds.', 'The time intervals in time series data can vary but are fixed, such as daily, weekly, hourly, or even sensor data captured every few milliseconds or microseconds.']}, {'end': 375.982, 'start': 233.017, 'title': 'Understanding time series data', 'summary': 'Discusses time series data, its components including trend, seasonality, and cyclicity, and the graphical representation of time series data.', 'duration': 142.965, 'highlights': ['Time series data is a sequence of data recorded over specific intervals, and it consists of four components: trend, seasonality, cyclicity, and irregularity.', 'Trend in time series data represents the overall change or pattern, which can be upward or downward.', 'Seasonality in time series data refers to periodic changes and certain patterns that occur over time.', 'The graphical representation of time series data includes plotting the data with time on the x-axis and price or stock price on the y-axis.']}], 'duration': 292.195, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf883787.jpg', 'highlights': ['Time series analysis is used to predict future events based on historical data such as stock prices and sales figures.', 'Time series data has time as a crucial component, and the time intervals are fixed, varying from daily to hourly or even milliseconds.', 'Time series data is a sequence of data recorded over specific intervals, and it consists of four components: trend, seasonality, cyclicity, and irregularity.', 'The graphical representation of time series data includes plotting the data with time on the x-axis and price or stock price on the y-axis.', 'The time intervals in time series data can vary but are fixed, such as daily, weekly, hourly, or even sensor data captured every few milliseconds or microseconds.', 'Trend in time series data represents the overall change or pattern, which can be upward or downward.', 'Seasonality in time series data refers to periodic changes and certain patterns that occur over time.']}, {'end': 554.164, 'segs': [{'end': 443.412, 'src': 'embed', 'start': 419.247, 'weight': 0, 'content': [{'end': 426.789, 'text': 'and then again around december, again they will increase and then the sales will come down, and then there will be again an increase,', 'start': 419.247, 'duration': 7.542}, {'end': 430.47, 'text': 'and then they will come down and then again an increase, and then they will come down.', 'start': 426.789, 'duration': 3.681}, {'end': 431.93, 'text': "let's say, this is the sales pattern.", 'start': 430.47, 'duration': 1.46}, {'end': 434.23, 'text': 'so you see, here there is a trend as well.', 'start': 431.93, 'duration': 2.3}, {'end': 435.831, 'text': 'there is an upward trend, right.', 'start': 434.23, 'duration': 1.601}, {'end': 437.631, 'text': 'the sales are increasing over.', 'start': 435.831, 'duration': 1.8}, {'end': 439.311, 'text': "let's say, these are multiple years.", 'start': 437.631, 'duration': 1.68}, {'end': 443.412, 'text': 'this is for year one, this is for year two, this is for year three and so on.', 'start': 439.311, 'duration': 4.101}], 'summary': 'Sales show a recurring pattern of increase and decrease over multiple years.', 'duration': 24.165, 'max_score': 419.247, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8419247.jpg'}, {'end': 480.249, 'src': 'embed', 'start': 454.314, 'weight': 1, 'content': [{'end': 459.357, 'text': 'every December, the sales are increasing or they are peaking for that particular year.', 'start': 454.314, 'duration': 5.043}, {'end': 461.118, 'text': 'right then there is a new year.', 'start': 459.357, 'duration': 1.761}, {'end': 464.259, 'text': 'again, when December approaches, the sales are increasing.', 'start': 461.118, 'duration': 3.141}, {'end': 468.102, 'text': 'again. when December approaches, the sales are increasing, and so on and so forth.', 'start': 464.259, 'duration': 3.843}, {'end': 470.263, 'text': 'so this is known as seasonality.', 'start': 468.102, 'duration': 2.161}, {'end': 475.866, 'text': 'so there is a certain fluctuation, which is which is periodic in nature.', 'start': 470.263, 'duration': 5.603}, {'end': 478.108, 'text': 'so this is known as seasonality.', 'start': 475.866, 'duration': 2.242}, {'end': 479.108, 'text': 'then cyclicity.', 'start': 478.108, 'duration': 1}, {'end': 480.249, 'text': 'what is cyclicity now?', 'start': 479.108, 'duration': 1.141}], 'summary': 'December sales peak annually due to seasonality, showing periodic fluctuation.', 'duration': 25.935, 'max_score': 454.314, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8454314.jpg'}, {'end': 530.455, 'src': 'embed', 'start': 500.97, 'weight': 2, 'content': [{'end': 506.055, 'text': 'However, cyclicity, what happens is, first of all, the duration is pretty much not fixed.', 'start': 500.97, 'duration': 5.085}, {'end': 512.581, 'text': 'And the duration or the gap length of time between two cycles can be much longer.', 'start': 506.395, 'duration': 6.186}, {'end': 514.722, 'text': 'So recession is an example.', 'start': 512.701, 'duration': 2.021}, {'end': 517.365, 'text': "So we had, let's say, recession in 2001 or 2002, perhaps.", 'start': 514.803, 'duration': 2.562}, {'end': 520.028, 'text': 'And then we had one in 2008.', 'start': 517.445, 'duration': 2.583}, {'end': 527.353, 'text': 'And then we had probably in 2000, 2003.', 'start': 520.028, 'duration': 7.325}, {'end': 528.834, 'text': '12 and so on and so forth.', 'start': 527.354, 'duration': 1.48}, {'end': 530.455, 'text': 'so it is not like every year.', 'start': 528.834, 'duration': 1.621}], 'summary': 'Economic cycles like recessions occur irregularly with varying durations, not annually.', 'duration': 29.485, 'max_score': 500.97, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8500970.jpg'}], 'start': 376.943, 'title': 'Sales seasonality and cyclicity', 'summary': 'Discusses the seasonal increase in warm clothes sales around december, explaining the concepts of seasonality and cyclicity, with seasonality showing an annual pattern and cyclicity exhibiting longer, non-fixed sales cycles, such as during a recession.', 'chapters': [{'end': 554.164, 'start': 376.943, 'title': 'Sales seasonality and cyclicity', 'summary': 'Discusses the sales pattern of warm clothes, illustrating the seasonal increase in sales around december and the concept of seasonality and cyclicity, with seasonality showing an annual pattern of sales increase and cyclicity exhibiting longer, non-fixed duration between sales cycles, such as in a recession.', 'duration': 177.221, 'highlights': ['The sales of warm clothes exhibit a seasonal pattern, with sales increasing around December and then decreasing, and this trend shows an overall upward trend over multiple years.', 'Seasonality is characterized by a periodic fluctuation in sales, typically occurring annually, such as the increase in sales around December each year.', 'Cyclicity, on the other hand, involves longer and non-fixed durations between sales cycles, such as in the case of recessions, where the slump and recovery do not follow an annual pattern.']}], 'duration': 177.221, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8376943.jpg', 'highlights': ['The sales of warm clothes exhibit a seasonal pattern, with sales increasing around December and then decreasing, and this trend shows an overall upward trend over multiple years.', 'Seasonality is characterized by a periodic fluctuation in sales, typically occurring annually, such as the increase in sales around December each year.', 'Cyclicity involves longer and non-fixed durations between sales cycles, such as in the case of recessions, where the slump and recovery do not follow an annual pattern.']}, {'end': 1204.832, 'segs': [{'end': 647.67, 'src': 'embed', 'start': 621.276, 'weight': 4, 'content': [{'end': 626.541, 'text': 'So what are the situations where we cannot do time series analysis?', 'start': 621.276, 'duration': 5.265}, {'end': 632.187, 'text': "So there will be some data which is collected over a period of time, but it's really not changing.", 'start': 626.601, 'duration': 5.586}, {'end': 638.657, 'text': 'So it will not really not make sense to perform any time series analysis over it.', 'start': 632.407, 'duration': 6.25}, {'end': 640.099, 'text': 'Right For example, like this one.', 'start': 638.677, 'duration': 1.422}, {'end': 647.67, 'text': 'So if we take X as the time and Y as the value of whatever the output we are talking about, and if the Y value is constant,', 'start': 640.239, 'duration': 7.431}], 'summary': 'Time series analysis cannot be performed on static data.', 'duration': 26.394, 'max_score': 621.276, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8621276.jpg'}, {'end': 728.91, 'src': 'embed', 'start': 702.897, 'weight': 0, 'content': [{'end': 710.941, 'text': 'if you recall from one of my earlier slides, we said that time series data has the following four components the trend, seasonality,', 'start': 702.897, 'duration': 8.044}, {'end': 714.883, 'text': 'cyclicity and random, random component or irregularity.', 'start': 710.941, 'duration': 3.942}, {'end': 723.567, 'text': 'right. so if these components are present in time series data, it is non-stationary, which means that typically these components will be present.', 'start': 714.883, 'duration': 8.684}, {'end': 728.91, 'text': 'therefore, most of the time a time series data that is collected raw data is non-stationary data,', 'start': 723.567, 'duration': 5.343}], 'summary': 'Time series data has trend, seasonality, cyclicity, and random components, making it non-stationary most of the time.', 'duration': 26.013, 'max_score': 702.897, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8702897.jpg'}, {'end': 909.538, 'src': 'embed', 'start': 883.845, 'weight': 2, 'content': [{'end': 890.128, 'text': 'So as again once again visually this is how it would look if the covariance is also changing with respect to time.', 'start': 883.845, 'duration': 6.283}, {'end': 891.389, 'text': 'so these are the three.', 'start': 890.268, 'duration': 1.121}, {'end': 893.83, 'text': 'all three components should be pretty much constant.', 'start': 891.389, 'duration': 2.441}, {'end': 901.174, 'text': 'that is when you have stationary data and in order to perform time series analysis, the data should be stationary, okay.', 'start': 893.83, 'duration': 7.344}, {'end': 909.538, 'text': "so let's take a look at, uh, the concept of moving average or the method of moving average, and let's see how it works.", 'start': 901.174, 'duration': 8.364}], 'summary': 'In time series analysis, stationary data with constant covariance is essential for moving average method.', 'duration': 25.693, 'max_score': 883.845, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8883845.jpg'}, {'end': 1197.985, 'src': 'embed', 'start': 1168.085, 'weight': 3, 'content': [{'end': 1171.567, 'text': 'okay, so that is the centered moving average.', 'start': 1168.085, 'duration': 3.482}, {'end': 1176.849, 'text': 'this is done primarily to smoothen the data so that there are not too many rough edges.', 'start': 1171.567, 'duration': 5.282}, {'end': 1178.13, 'text': 'so that is what we do here.', 'start': 1176.849, 'duration': 1.281}, {'end': 1182.973, 'text': 'So if we visualize this data now, this is how it looks.', 'start': 1178.55, 'duration': 4.423}, {'end': 1189.098, 'text': 'Right So if we take the centered moving average, as you can see, there is a gradual increase.', 'start': 1183.394, 'duration': 5.704}, {'end': 1193.461, 'text': 'If this was not the case, if we had not centered it, the changes would have been much sharper.', 'start': 1189.198, 'duration': 4.263}, {'end': 1197.985, 'text': 'So that is basically the smoothening that we are talking about.', 'start': 1193.681, 'duration': 4.304}], 'summary': 'Centered moving average smoothens data, reducing sharp changes.', 'duration': 29.9, 'max_score': 1168.085, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf81168085.jpg'}], 'start': 554.164, 'title': 'Time series analysis', 'summary': 'Covers components of time series data, including trend, cyclicity, seasonality, and irregularity, and the concept of non-stationary data, with a detailed example of car sales data. it also discusses the limitations of time series analysis in cases of constant data or predictable patterns.', 'chapters': [{'end': 662.423, 'start': 554.164, 'title': 'Understanding time series analysis', 'summary': 'Explains the components of time series data, including trend, cyclicity, seasonality, and irregularity, and discusses situations where time series analysis cannot be applied due to constant data or predictable patterns.', 'duration': 108.259, 'highlights': ['The components of time series data include trend, cyclicity, seasonality, and irregularity, with irregularity representing the random component of the data.', 'Time series analysis cannot be applied to data that remains constant over time, as well as data that follows a predictable function such as a sine wave or a cosine wave.']}, {'end': 1204.832, 'start': 662.423, 'title': 'Stationarity in time series analysis', 'summary': 'Discusses the need for stationary data in time series analysis, explaining the concept of non-stationary data and the components that make it non-stationary. it also covers the concept of moving average and its application in forecasting, using a detailed example of car sales data.', 'duration': 542.409, 'highlights': ['Non-stationary time series data contains trend, seasonality, cyclicity, and random components, affecting time series forecasting.', 'The mean, variance, and covariance of data determine its stationarity in time series analysis.', 'Moving average involves taking the average of a specific number of consecutive data points, and centered moving average is used for smoothening the data.']}], 'duration': 650.668, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf8554164.jpg', 'highlights': ['Non-stationary time series data contains trend, seasonality, cyclicity, and random components, affecting time series forecasting.', 'The components of time series data include trend, cyclicity, seasonality, and irregularity, with irregularity representing the random component of the data.', 'The mean, variance, and covariance of data determine its stationarity in time series analysis.', 'Moving average involves taking the average of a specific number of consecutive data points, and centered moving average is used for smoothening the data.', 'Time series analysis cannot be applied to data that remains constant over time, as well as data that follows a predictable function such as a sine wave or a cosine wave.']}, {'end': 1539.274, 'segs': [{'end': 1253.347, 'src': 'embed', 'start': 1204.832, 'weight': 1, 'content': [{'end': 1216.319, 'text': 'what we will do is we will take the centered moving average as our baseline and then start doing a few more calculations that are required in order to come up with the prediction.', 'start': 1204.832, 'duration': 11.487}, {'end': 1224.781, 'text': 'so what we are going to do is we are going to use this multiplicity or multiplicative model in this case, and this is how it it looks.', 'start': 1216.319, 'duration': 8.462}, {'end': 1236.403, 'text': 'so we take the product of seasonality and the trend and the irregularity components and we just multiply that and in order to get that,', 'start': 1224.781, 'duration': 11.622}, {'end': 1242.504, 'text': 'this product of these two, We have basically the actual value divided by CMA.', 'start': 1236.403, 'duration': 6.101}, {'end': 1249.106, 'text': 'Yt value divided by CMA will give you the predicted value of Yt is equal to the product of all three components.', 'start': 1242.884, 'duration': 6.222}, {'end': 1253.347, 'text': 'Therefore, St into Yt is equal to Yt by CMA.', 'start': 1249.326, 'duration': 4.021}], 'summary': 'Using a multiplicative model, we predict yt by dividing its actual value by cma and multiplying the components, resulting in st * yt = yt / cma.', 'duration': 48.515, 'max_score': 1204.832, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf81204832.jpg'}, {'end': 1362.306, 'src': 'heatmap', 'start': 1296.077, 'weight': 0.839, 'content': [{'end': 1300.922, 'text': 'so if you see, these values are nothing but the yt value divided by cma.', 'start': 1296.077, 'duration': 4.845}, {'end': 1303.405, 'text': 'so in this case it is 4 by 3.5, which is 1.14.', 'start': 1300.922, 'duration': 2.483}, {'end': 1305.547, 'text': 'similarly, 4.5 by 3.7, 1.22, and so on and so forth.', 'start': 1303.405, 'duration': 2.142}, {'end': 1320.986, 'text': 'so we take we have the product st into it and then the next step is to calculate the average of respective quarters.', 'start': 1305.547, 'duration': 15.439}, {'end': 1324.77, 'text': 'so that is what we are doing here average of respective quarters.', 'start': 1320.986, 'duration': 3.784}, {'end': 1329.194, 'text': 'and then we need to calculate the de-seasonalized values.', 'start': 1324.77, 'duration': 4.424}, {'end': 1335.341, 'text': 'So in order to get decisionalized value, we need to divide YT by ST.', 'start': 1329.614, 'duration': 5.727}, {'end': 1336.462, 'text': 'that was calculated.', 'start': 1335.341, 'duration': 1.121}, {'end': 1339.345, 'text': 'So for example, here it is 2.8 by 0.9.', 'start': 1336.482, 'duration': 2.863}, {'end': 1348.296, 'text': 'So we got the decisionalized value here and then we get the trend and then we get the predicted values.', 'start': 1339.345, 'duration': 8.951}, {'end': 1356.782, 'text': 'so, in order to get the predicted value, which is basically, we predict the values for known values as well, like, for example, year one, quarter one.', 'start': 1348.576, 'duration': 8.206}, {'end': 1362.306, 'text': 'we know the value, but now that we have our model, we predict ourselves and see how close it is.', 'start': 1356.782, 'duration': 5.524}], 'summary': 'Calculating seasonal and deseasonalized values for predictions.', 'duration': 66.229, 'max_score': 1296.077, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf81296077.jpg'}, {'end': 1420.726, 'src': 'embed', 'start': 1392.503, 'weight': 0, 'content': [{'end': 1395.244, 'text': 'So, for example, the predicted value is this gray color here.', 'start': 1392.503, 'duration': 2.741}, {'end': 1400.726, 'text': 'And you can see that it is actually pretty much following the actual value, which is the blue color.', 'start': 1395.644, 'duration': 5.082}, {'end': 1402.807, 'text': 'And the gray color is the predicted value.', 'start': 1401.326, 'duration': 1.481}, {'end': 1412.677, 'text': 'So wherever we know the values up to year four, we can see that our predicted values are following or pretty much very close to the actual values.', 'start': 1402.907, 'duration': 9.77}, {'end': 1415.8, 'text': 'And then from here onwards, when the year five starts.', 'start': 1412.897, 'duration': 2.903}, {'end': 1420.726, 'text': "the blue color line is not there because we don't have the actual values, only the predicted values.", 'start': 1415.8, 'duration': 4.926}], 'summary': 'Predicted values closely match actual values up to year four.', 'duration': 28.223, 'max_score': 1392.503, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf81392503.jpg'}, {'end': 1477.292, 'src': 'heatmap', 'start': 1451.128, 'weight': 0.783, 'content': [{'end': 1457.353, 'text': 'So we calculated the ST, IT, the product of ST and IT using the formula like here.', 'start': 1451.128, 'duration': 6.225}, {'end': 1459.654, 'text': 'Y by YT by CMA.', 'start': 1457.853, 'duration': 1.801}, {'end': 1460.575, 'text': 'We got that.', 'start': 1459.914, 'duration': 0.661}, {'end': 1464.318, 'text': 'And then we got ST, which is basically YT.', 'start': 1460.855, 'duration': 3.463}, {'end': 1473.468, 'text': 'So this is the average of the first quarters for all the four years, and similarly, this is the average of the second quarter for all the four years,', 'start': 1464.778, 'duration': 8.69}, {'end': 1473.908, 'text': 'and so on.', 'start': 1473.468, 'duration': 0.44}, {'end': 1475.37, 'text': 'So these values are repeating.', 'start': 1473.948, 'duration': 1.422}, {'end': 1477.292, 'text': 'They are calculated only once.', 'start': 1475.61, 'duration': 1.682}], 'summary': 'Calculated st, it, and product using the given formula to obtain average quarterly values.', 'duration': 26.164, 'max_score': 1451.128, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf81451128.jpg'}], 'start': 1204.832, 'title': 'Time series forecasting analysis', 'summary': "Delves into using a multiplicative model to predict values in time series data, calculating seasonal and irregular components, de-seasonalizing data, and evaluating the model's accuracy with predicted values closely aligned with actual values.", 'chapters': [{'end': 1539.274, 'start': 1204.832, 'title': 'Time series forecasting analysis', 'summary': "Delves into using a multiplicative model to predict values in time series data, calculating seasonal and irregular components, de-seasonalizing data, and evaluating the model's accuracy with predicted values closely aligned with actual values.", 'duration': 334.442, 'highlights': ['Using a multiplicative model to predict values in time series data', 'Calculating seasonal and irregular components', "Evaluating the model's accuracy with predicted values closely aligned with actual values"]}], 'duration': 334.442, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf81204832.jpg', 'highlights': ["Evaluating the model's accuracy with predicted values closely aligned with actual values", 'Calculating seasonal and irregular components', 'Using a multiplicative model to predict values in time series data']}, {'end': 1966.704, 'segs': [{'end': 1600.145, 'src': 'embed', 'start': 1539.274, 'weight': 1, 'content': [{'end': 1548.279, 'text': 'we will actually use what is known as a regression tool or analytics tool that is available in excel.', 'start': 1539.274, 'duration': 9.005}, {'end': 1551.181, 'text': 'so you remember, we have our data in excel.', 'start': 1548.279, 'duration': 2.902}, {'end': 1560.306, 'text': 'so let me take you to the excel, and here we need to calculate the intercept and the slope.', 'start': 1551.181, 'duration': 9.125}, {'end': 1566.43, 'text': 'in order to do that, we have to use the regression mechanism and in order to use the regression mechanism,', 'start': 1560.306, 'duration': 6.124}, {'end': 1570.632, 'text': 'we have to use the analytics tool that comes with excel.', 'start': 1566.43, 'duration': 4.202}, {'end': 1573.034, 'text': 'so how do you activate this tool?', 'start': 1570.632, 'duration': 2.402}, {'end': 1577.357, 'text': 'so this is how you would need to activate the tool from excel.', 'start': 1573.034, 'duration': 4.323}, {'end': 1583.881, 'text': 'you need to go to options and in options there will be add-ins,', 'start': 1577.357, 'duration': 6.524}, {'end': 1596.905, 'text': 'and in add-ins you will have um analysis tool pack and you select this and you just say go, it will open up a box like this.', 'start': 1583.881, 'duration': 13.024}, {'end': 1600.145, 'text': 'you say analysis tool pack and you say okay.', 'start': 1596.905, 'duration': 3.24}], 'summary': "Using excel's regression tool to calculate intercept and slope for data analysis.", 'duration': 60.871, 'max_score': 1539.274, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf81539274.jpg'}, {'end': 1805.434, 'src': 'heatmap', 'start': 1724.487, 'weight': 4, 'content': [{'end': 1729.69, 'text': 'so our trend is equal to intercept plus slope into the time code.', 'start': 1724.487, 'duration': 5.203}, {'end': 1735.512, 'text': 'so the intercept is, uh out here, as we can see in our slide as well.', 'start': 1729.69, 'duration': 5.822}, {'end': 1740.374, 'text': 'so if you see here, this is our intercept and the lower value is the slope.', 'start': 1735.512, 'duration': 4.862}, {'end': 1743.755, 'text': "so we have calculated here and it's shown in the slides as well.", 'start': 1740.374, 'duration': 3.381}, {'end': 1744.956, 'text': 'so intercept.', 'start': 1743.755, 'duration': 1.201}, {'end': 1747.297, 'text': 'so the formula is shown here.', 'start': 1744.956, 'duration': 2.341}, {'end': 1752.419, 'text': 'so our trend is equal to intercept plus slope into time code.', 'start': 1747.297, 'duration': 5.122}, {'end': 1756.34, 'text': 'time code is nothing but this one t column a one, two, three, four, okay.', 'start': 1752.419, 'duration': 3.921}, {'end': 1764.262, 'text': "so that's how you calculate the trend, and that's how you use the data analysis tool from excel.", 'start': 1756.84, 'duration': 7.422}, {'end': 1773.185, 'text': 'using these two, we calculate the predicted values and using this formula, which is basically, trend is equal to intercept plus slope into time code,', 'start': 1764.262, 'duration': 8.923}, {'end': 1778.547, 'text': 'and then we can go and plot it, see how it is looking.', 'start': 1773.185, 'duration': 5.362}, {'end': 1789.39, 'text': 'and therefore So we see here that the predicted values are pretty close to the actual values and therefore we can safely assume that our calculations,', 'start': 1778.547, 'duration': 10.843}, {'end': 1796.232, 'text': 'which are like our manual model, is working and hence we go ahead and predict for the fifth year.', 'start': 1789.39, 'duration': 6.842}, {'end': 1805.434, 'text': "so till four years we know the actual value as well, so we can compare our model is performing and for the fifth year we don't have reference values.", 'start': 1796.232, 'duration': 9.202}], 'summary': 'Using regression analysis, predicted values closely match actual values, validating the model for future predictions.', 'duration': 27.932, 'max_score': 1724.487, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf81724487.jpg'}, {'end': 1805.434, 'src': 'embed', 'start': 1778.547, 'weight': 0, 'content': [{'end': 1789.39, 'text': 'and therefore So we see here that the predicted values are pretty close to the actual values and therefore we can safely assume that our calculations,', 'start': 1778.547, 'duration': 10.843}, {'end': 1796.232, 'text': 'which are like our manual model, is working and hence we go ahead and predict for the fifth year.', 'start': 1789.39, 'duration': 6.842}, {'end': 1805.434, 'text': "so till four years we know the actual value as well, so we can compare our model is performing and for the fifth year we don't have reference values.", 'start': 1796.232, 'duration': 9.202}], 'summary': 'Predicted values close to actual, model working, can predict for fifth year.', 'duration': 26.887, 'max_score': 1778.547, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf81778547.jpg'}, {'end': 1966.704, 'src': 'embed', 'start': 1917.238, 'weight': 3, 'content': [{'end': 1926.603, 'text': 'we did not use any tool or any standard model, but this is just to understand how time series analysis work and hope you enjoyed this video.', 'start': 1917.238, 'duration': 9.365}, {'end': 1937.146, 'text': 'thank you very much for joining the session and in the next video we will talk about how to perform this in using R and using a proper model like ARIMA,', 'start': 1926.603, 'duration': 10.543}, {'end': 1940.027, 'text': 'and hopefully you will enjoy that as well.', 'start': 1937.146, 'duration': 2.881}, {'end': 1941.127, 'text': "so that's it for now.", 'start': 1940.027, 'duration': 1.1}, {'end': 1941.968, 'text': 'thank you very much.', 'start': 1941.127, 'duration': 0.841}, {'end': 1947.509, 'text': 'if you have any comments, please put your comments down below, including your contact email.', 'start': 1941.968, 'duration': 5.541}, {'end': 1952.331, 'text': 'if you need any response, we can respond to you and hope to see you soon.', 'start': 1947.509, 'duration': 4.822}, {'end': 1953.151, 'text': 'thank you very much.', 'start': 1952.331, 'duration': 0.82}, {'end': 1958.502, 'text': 'bye, bye, Hi there.', 'start': 1953.151, 'duration': 5.351}, {'end': 1964.418, 'text': 'if you like this video, subscribe to the Simply Learn YouTube channel and click here to watch similar videos.', 'start': 1958.502, 'duration': 5.916}, {'end': 1966.704, 'text': 'To nerd up and get certified, click here.', 'start': 1964.719, 'duration': 1.985}], 'summary': 'Introduction to time series analysis without tools. next video: r and arima model.', 'duration': 49.466, 'max_score': 1917.238, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf81917238.jpg'}], 'start': 1539.274, 'title': 'Using regression tool in excel and time series forecasting summary', 'summary': 'Covers how to use regression tool in excel for data analysis and prediction, and demonstrates manual time series analysis to forecast sales, achieving close alignment between predicted and actual values for the fifth year, and introducing time series data components and stationarity.', 'chapters': [{'end': 1778.547, 'start': 1539.274, 'title': 'Using regression tool in excel', 'summary': 'Explains how to activate and use the regression tool in excel for calculating intercept, slope, and trend, enabling data analysis and prediction.', 'duration': 239.273, 'highlights': ['The process of activating the analysis tool pack in Excel is demonstrated, allowing data analysis to be performed.', "The method for calculating the trend using the formula 'trend = intercept + slope * time code' is explained.", 'The importance of using the regression tool to calculate predicted values and plot trends is emphasized.']}, {'end': 1966.704, 'start': 1778.547, 'title': 'Time series forecasting summary', 'summary': 'Demonstrates manual time series analysis to forecast sales using a moving average method, showing close alignment between predicted and actual values for the fifth year, and introduces the concept of time series data, components, and stationarity, with plans to explore arima model in the next video.', 'duration': 188.157, 'highlights': ['The manual time series analysis method demonstrates close alignment between predicted and actual values for the fifth year.', 'Introduction of time series data, components, and stationarity, aiming to explore ARIMA model in the next video.', 'Encouragement for feedback and subscription to the Simply Learn YouTube channel.']}], 'duration': 427.43, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/gj4L2isnOf8/pics/gj4L2isnOf81539274.jpg', 'highlights': ['The manual time series analysis method demonstrates close alignment between predicted and actual values for the fifth year.', 'The process of activating the analysis tool pack in Excel is demonstrated, allowing data analysis to be performed.', 'The importance of using the regression tool to calculate predicted values and plot trends is emphasized.', 'Introduction of time series data, components, and stationarity, aiming to explore ARIMA model in the next video.', "The method for calculating the trend using the formula 'trend = intercept + slope * time code' is explained.", 'Encouragement for feedback and subscription to the Simply Learn YouTube channel.']}], 'highlights': ['The sales of warm clothes exhibit a seasonal pattern, with sales increasing around December and then decreasing, and this trend shows an overall upward trend over multiple years.', 'Time series analysis is used to predict future events based on historical data such as stock prices and sales figures.', 'Time series data is a sequence of data recorded over specific intervals, and it consists of four components: trend, seasonality, cyclicity, and irregularity.', 'The chapter covers the significance of time series analysis, components of time series data, and the process of making time series data stationary.', 'The graphical representation of time series data includes plotting the data with time on the x-axis and price or stock price on the y-axis.', 'The manual time series analysis method demonstrates close alignment between predicted and actual values for the fifth year.', "Evaluating the model's accuracy with predicted values closely aligned with actual values", 'The process of activating the analysis tool pack in Excel is demonstrated, allowing data analysis to be performed.', 'The importance of using the regression tool to calculate predicted values and plot trends is emphasized.', 'Using a multiplicative model to predict values in time series data']}