Yahoo Web Search

Search results

1. Top results related to define moving average method in time series design diagram

   • Average in time series based on time and date in R

     Top Answer

     Answered Dec 17, 2012 · 11 votes

     Assuming your dataset is called temp and that your "date_time" variable is a proper date format (done using, say, as.POSIXlt(temp$date_time), then you can simply use aggregate and cut to get hourly summaries:

     aggregate(list(temperature = temp$temperature), list(hourofday = cut(temp$date_time, "1 hour")), mean)# hourofday temperature# 1 2007-09-29 00:00:00 -1.744333# 2 2007-09-29 01:00:00 -1.586000# 3 2007-09-29 02:00:00 -1.751667# 4 2007-09-29 03:00:00 -1.820000

     Check all answers on Stack Overflow

     1/5

   • Moving Average of time series using a sliding window over an array

     Top Answer

     Answered Oct 13, 2019 · 2 votes

     So something following this logic should probably be a good starting point. Hope this is a helpful start, and welcome to the cs community.

     def sliding_window( dates, numbers, sliding_window_value):- # list of dictionaries return_dicts =[{}] # if window size is greater than length of dates, there's only one window if sliding_window_value >= len(dates): return_dicts += [create_window(dates, numbers)] return return_dicts # gather all our windows into one list for i in range (0, len(dates) - sliding_window_value ): # get our window subsets dates_subset = dates[i:(sliding_window_value+1)] numbers_subset = numbers[i:(sliding_window_value+1)] # get our window stats dictionary window_stats = create_window(dates_subset,numbers_subset) # add these stats to our return list return_dicts += [window_stats] return return_dictsdef create_window(dates_subset, numbers_subset): window_min = 1000000 # some high minimum to start window_max = -1000000 # some low maximuim to start window_total = 0 for i in range ( 0, len(dates_subset)): # calculate total window_total += numbers_subset[i] # calculate max if numbers_subset[i] > window_max: window_max = numbers_subset[i] # calculate min if numbers_subset[i] < window_min: window_min = numbers_subset[i] # other calculations.... return_dict = { "min" : window_min, "max" : window_max, "average" : window_total / len(dates_subset), # other calculations.... } return return_dict

     Good luck bud, the work is worth it.

     Check all answers on Stack Overflow

     2/5

   • Average of two time series in pandas

     Top Answer

     Answered Jul 02, 2020 · 1 votes

     To perform the task 1) you can do this:

     #union of the indexes union_idx = green_df.index.union(red_df.index)-- #reindex with the union green_df= green_df.reindex(union_idx) red_df= red_df.reindex(union_idx) # the interpolation green_df = green_df.interpolate(method='linear', limit_direction='forward', axis=0) red_df = red_df.interpolate(method='linear', limit_direction='forward', axis=0) grey_df= pd.concat([green_df,red_df]) grey_df= grey_df.groupby(level=0).mean()

     I get (i didn't pay attention to displaying the correct colors)

     Check all answers on Stack Overflow

     3/5

   • Method of finding gaps in time series data in MySQL?

     Top Answer

     Answered Jun 18, 2012 · 20 votes

     To start with, let us summarize the number of entries by hour in your table.

     SELECT CAST(DATE_FORMAT(entry_time,'%Y-%m-%d %k:00:00') AS DATETIME) hour, COUNT(*) samplecount FROM table GROUP BY CAST(DATE_FORMAT(entry_time,'%Y-%m-%d %k:00:00') AS DATETIME)-

     Now, if you log something every six minutes (ten times an hour) all your samplecount values should be ten. This expression: CAST(DATE_FORMAT(entry_time,'%Y-%m-%d %k:00:00') AS DATETIME) looks hairy but it simply truncates your timestamps to the hour in which they occur by zeroing out the minute and second.

     This is reasonably efficient, and will get you started. It's very efficient if you can put an index on your entry_time column and restrict your query to, let's say, yesterday's samples as shown here.

     SELECT CAST(DATE_FORMAT(entry_time,'%Y-%m-%d %k:00:00') AS DATETIME) hour, COUNT(*) samplecount FROM table WHERE entry_time >= CURRENT_DATE - INTERVAL 1 DAY AND entry_time < CURRENT_DATE GROUP BY CAST(DATE_FORMAT(entry_time,'%Y-%m-%d %k:00:00') AS DATETIME)

     But it isn't much good at detecting whole hours that go by with missing samples. It's also a little sensitive to jitter in your sampling. That is, if your top-of-the-hour sample is sometimes a half-second early (10:59:30) and sometimes a half-second late (11:00:30) your hourly summary counts will be off. So, this hour summary thing (or day summary, or minute summary, etc) is not bulletproof.

     You need a self-join query to get stuff perfectly right; it's a bit more of a hairball and not nearly as efficient.

     Let's start by creating ourselves a virtual table (subquery) like this with numbered samples. (This is a pain in MySQL; some other expensive DBMSs make it easier. No matter.)

     SELECT @sample:=@sample+1 AS entry_num, c.entry_time, c.value FROM ( SELECT entry_time, value FROM table ORDER BY entry_time ) C, (SELECT @sample:=0) s

     This little virtual table gives entry_num, entry_time, value.

     Next step, we join it to itself.

     SELECT one.entry_num, one.entry_time, one.value, TIMEDIFF(two.value, one.value) interval FROM ( /* virtual table */ ) ONE JOIN ( /* same virtual table */ ) TWO ON (TWO.entry_num - 1 = ONE.entry_num)

     This lines up the tables next two each other offset by a single entry, governed by the ON clause of the JOIN.

     Finally we choose the values from this table with an interval larger than your threshold, and there are the times of the samples right before the missing ones.

     The over all self join query is this. I told you it was a hairball.

     SELECT one.entry_num, one.entry_time, one.value, TIMEDIFF(two.value, one.value) interval FROM ( SELECT @sample:=@sample+1 AS entry_num, c.entry_time, c.value FROM ( SELECT entry_time, value FROM table ORDER BY entry_time ) C, (SELECT @sample:=0) s ) ONE JOIN ( SELECT @sample2:=@sample2+1 AS entry_num, c.entry_time, c.value FROM ( SELECT entry_time, value FROM table ORDER BY entry_time ) C, (SELECT @sample2:=0) s ) TWO ON (TWO.entry_num - 1 = ONE.entry_num)

     If you have to do this in production on a large table you may want to do it for a subset of your data. For example, you could do it each day for the previous two days' samples. This would be decently efficient, and would also make sure you didn't overlook any missing samples right at midnight. To do this your little rownumbered virtual tables would look like this.

     SELECT @sample:=@sample+1 AS entry_num, c.entry_time, c.value FROM ( SELECT entry_time, value FROM table ORDER BY entry_time WHERE entry_time >= CURRENT_DATE - INTERVAL 2 DAY AND entry_time < CURRENT_DATE /*yesterday but not today*/ ) C, (SELECT @sample:=0) s

     Check all answers on Stack Overflow

     4/5

   • Understanding the Moving average (MA) in Time Series Data

     Data is often collected with respect to time, whether for scientific or financial purposes. When data is collected in a chronological order, it is referred to as time series data. Analyzing time series data provides insights into how the data behaves over time, including underlying patterns that can help solve problems in various domains. Time series analysis can also aid in forecasting future values based on historical data, leading to better production, profits, policy planning, risk management, and other fields. Therefore, analysis of time series data becomes an important aspect of data science.

     In this article, we will discuss Moving Average Models, which are essential for time series analysis and forecasting trends.

     What is the Moving Average Model?Moving Average Models are a type of time series analysis model usually used in econometrics to forecast trends and understand patterns in time series data. In moving average models the present value of the time series depends on the linear combination of the past white noise error terms of the time series. In time series analysis moving average is denoted by the letter “q” which represents the order of the...

     • is the value of time series at time t
     • c is a constant or the mean of the time series
     • are the white noise terms associated with the time series at time t, t-1, t-2, … , t-q.
     • are the moving average constants.
     • First that the past noise term affects the MA model’s present value directly as we can see in the above equation of the MA model but in AR model the past noise term() have an indirect influence on the present AR model value() since the AR model equation depends on the previous value of the model() and the previous model value depends on it’s noise term().
     • The MA model works a a finite impulse model, which means that the current noise value affects the present value of the model as well as “q” further values, as the moving average models only depend on q terms in the past. Whereas AR models acts as infinite impulse model since the current noise affects infinite values of the model in the future. In AutoRegressive model value affects the term which affects the term and so on.
     • Stationarity: Stationarity is the principle of time series data that conveys that the statistical properties of the data doesn’t change with time, the mean of the data remains the same or we can also say that the data fluctuates around a certain value, the standard deviation of the time series data nearly remains constant, and there must not be any seasonality in the time series data or there is no periodic behavior in the data. We can check for the stationarity of the dataset visually as well as through Augmented Dickey-Fuller(ADF) Test. We consider stationarity to be one of the most important aspect that the time series data must possess in order to be accepted by the models that are applied to time series data for accurate modelling.
     • Differencing: Differencing is one of the most important steps to consider during time series analysis, after taking a peek at the original time series data, if the data is not stationary and contains a lot of trends then differencing must be considered since for accurate time series data analysis the data must be stationary. In regular differencing the current time series data is subtracted by the previous data point. , this method removes trends from the data, making it suitable for modelling.
     • White Noise: White noise is the error term which has the mean of zero and a constant standard deviation with no correlation of the data points with each other. White noise acts as a benchmark in the forecasting process through time series modelling, if the forecast error is nor white noise further modifications could be performed on the model, but if it reaches a state such that the forecast errors are white noise then the model would need no further improvements. The value of white noise series are random and unpredictable therefore if any time series data is a white noise then there is no method to model or forecast it.
     • ACF Plot: Autocorrelation Function plot or the ACF plot is the plot of correlation between the time series and its lagged version. It shows how similar the time series is with it’s different lagged values. Here the lag term is a fixed time displacement, in the ACF plot the x-axis is the lagged time series and the y-axis is the correlation which ranges from -1 to 1.
     • Noise Reduction: Moving average models are effective in smoothing out the noise which is present in the time series data, thus, it helps in reducing the short term fluctuations in the time series data.
     • Stationarity: Moving average models within itself assumes that the time series data is stationary which is a primary requirement in the time series analysis.
     • Ease of Understanding: It is not that tough to understand how the moving average models work in comparison to some of the other time series forecasting models. The order of the moving average model is the number of previous error terms on which the current value of the time series depends.
     • Combination of models: The moving average models could be easily combined with autoregressive and integrated models and the whole of ARIMA modelling could be applied on the time series data.
     • Assumption of Stationarity: In moving average models it is already assumed that the time series data is stationary, but in reality, most of the real world data are not stationary. Therefore, in order to convert the time series data into stationary data information loss might happen.
     • Limited Forecasting Range: Moving average models are suitable for short term forecasting, as the time increases model’s forecasting capabilities decreases.
     • Sensitivity to Outliers
     • Identification of Model Order: If the model order is not chosen correctly the model might not be able to predict the time series data effectively.

     Check all answers on Geeksforgeeks

     5/5

   Show moreShow less

2. Images

   • 
   • 
   • 
   • 

   View all

3. www.geeksforgeeks.org · understanding-the-movingUnderstanding the Moving average (MA) in Time Series Data

   www.geeksforgeeks.org · understanding-the-moving

   

   Feb 6, 2024 · In time series analysis, a moving average is a widely used technique to smooth out short-term fluctuations and highlight longer-term trends or cycles. R provides several ways to compute and visualize moving averages alongside time series data. This article will guide you through the process of adding a moving average plot to a time series plot in R

4. Searches related to define moving average method in time series design diagram

   define moving average method in time series design diagram example	define moving average method in time series design diagram meaning
   define moving average method in time series design diagram calculator	define moving average method in time series design diagram generator
   define moving average method in time series design diagram template	define moving average method in time series design diagram explained
   define moving average method in time series design diagram worksheet	define moving average method in time series design diagram analysis

5. 365datascience.com · tutorials · time-seriesWhat Is a Moving Average Model? – 365 Data Science

   365datascience.com · tutorials · time-series

   

   • What Is RT?
   • What Is C?
   • What Is Θ1?
   • What Is ϵtand ϵt-1?
   • So, How Do We Generate These residuals?

   For starters, rtrepresents the values of “r” in the current period - t. In terms of returns, it’s what we’re estimating the returns for today will be.

   See full list on 365datascience.com

   The first thing we see on the right side of the model is “c” - this stands for a constant factor. Of course, this is just the general representation and we would substitute this with a numeric value when we’re actually modeling data.

   See full list on 365datascience.com

   Next, θ1 is a numeric coefficient for the value associated with the 1st lag. We prefer not to use ϕ1like in the Autoregressive model, to avoid confusion.

   See full list on 365datascience.com

   Then come ϵt and ϵt-1which represent the residuals for the current and the previous period, respectively. For anybody not familiar with the term, a residual is the same as an error term – it expresses the difference between the observed value for a variable and our estimation. In this specific case: ϵt-1 = rt-1 - r̂t-1 , where r̂t-1represents our e...

   See full list on 365datascience.com

   It’s quite simple. We start from the beginning of the dataset r1 and try to predict each value (r̂2, r̂3, etc). Depending on how far off we were each time, we get a residual ϵt = rt - r̂t. Therefore, we generate these residuals as we go through the set and create the ϵ variable as we move through time (from period 1, all the way up to the current p...

   See full list on 365datascience.com

6. Videos

   • 

     25:30

     YouTube

     Introduction to Moving Average Method|Time Series|Statistics|Dream Maths

     Apr 17, 2022

     130.4K Views

   • 

     13:19

     YouTube

     Time Series 101: Introduction to Moving Averages (3MA), A Visual Guide

     Mar 13, 2023

     8.9K Views

   • 

     21:00

     YouTube

     Time Series 101: Exponential Moving Average, A Visual Guide

     Apr 2, 2023

     5.7K Views

   View all

7. towardsdatascience.com · the-comprehensive-guideThe Comprehensive Guide to Moving Averages in Time Series ...

   towardsdatascience.com · the-comprehensive-guide

   

   Oct 30, 2023 · Moving averages serve several purposes in time series analysis, such as noise reduction, seasonal decomposition, forecasting, outlier filtering, and creating smoother visualizations. The simple moving average assigns equal weight to observations from both the distant and recent past.

   • Author: Eryk Lewinson

8. en.wikipedia.org · wiki · Moving-average_modelMoving-average model - Wikipedia

   en.wikipedia.org · wiki · Moving-average_model

   

   In time series analysis, the moving-average model (MA model), also known as moving-average process, is a common approach for modeling univariate time series. [1] [2] The moving-average model specifies that the output variable is cross-correlated with a non-identical to itself random-variable.

9. medium.com · 365datascience · what-is-a-movingWhat Is a Moving Average Model? - Medium

   medium.com · 365datascience · what-is-a-moving

   

   Apr 1, 2020 · 1. What do we use the Moving Average model for? In time-series, we sometimes observe similarities between past errors and present values. That’s because certain unpredictable events happen,...

10. library.fiveable.me · key-terms · intro-time-seriesMoving average model - Vocab, Definition, and Must ... - Fiveable

    library.fiveable.me · key-terms · intro-time-series

    

    A moving average model is a statistical method used in time series analysis that focuses on the relationship between an observation and a residual error from a moving average of past observations. It helps to smooth out short-term fluctuations and highlight longer-term trends or cycles in data.

11. medium.com · @wainaina · the-complete-guideThe Complete Guide to Time Series Forecasting Models

    medium.com · @wainaina · the-complete-guide

    

    Oct 24, 2023 · Time series forecasting involves analyzing data that evolves over some period of time and then utilizing statistical models to make predictions about future patterns and trends. It takes into...