# Forecast with AR, MA and ARIMA models look all equal and false

Hi all,

I just wanted to use time series models to forecast electricity prices. I use the forecast package and I created several models like:

ar10_model<-Arima(ts(generationData$Price), order=c(10,0,0)) ar5_model<-Arima(ts(generationData$Price), order=c(5,0,0))
ar1_model<-Arima(ts(generationData$Price), order=c(1,0,0)) ma10_model<-Arima(ts(generationData$Price), order=c(0,0,10))
ma5_model<-Arima(ts(generationData$Price), order=c(0,0,5)) ma1_model<-Arima(ts(generationData$Price), order=c(0,0,1))
Strangley they all look equal and wrong, when using the forecast plot
futurVal <- forecast(ar5_model,h=1000, level=c(99.5))
plot(futurVal)

Here you see the exemplary output of an AR(5) model

Then I used the auto.arima function of the forecast package. Here you see the command and the output

auto.arima(energyData$Price)  Series: energyData$Price
ARIMA(4,1,4)

Coefficients:
ar1      ar2     ar3      ar4      ma1     ma2      ma3     ma4
0.8569  -0.5476  0.8143  -0.5459  -0.4659  0.3358  -0.8241  0.1923
s.e.  0.0501   0.0891  0.1044   0.0491   0.0510  0.0747   0.0686  0.0241

sigma^2 estimated as 18.41:  log likelihood=-25179.78
AIC=50377.57   AICc=50377.59   BIC=50441.26


So I created an ARIMA (4,1,4) model by using the following code:

arima414_model<-Arima(ts(generationData$Price), order=c(4,1,4)) futurVal <- forecast(arima414_model,h=1000, level=c(99.5)) But the forecast is also extremely faulty, as you can see in the plot Why are the forecasts useless and do not really forecast the values of the time series is a good way? It should be obvious for the fitting algrithms in R that those forecasts are extremely wrong. Can anyone give me some adivce how to do a better forecast using time series models? I'd appreciate every comment. The best way to understand this is to work the examples in the fpp2 book, paying close attention to diagnostics for autocorrelation and ways to make a series stationary using the method illustrated in Fig. 8.11. One thing that pops out is the unrealistic h in the forecast for the ma5_model. 2 Likes AR and MA effects describe the inertia from one time period to the next, whether it is in the variable itself (AR) or in the random effects (MA). By their very definition, these effects are short-lived. The MA(4) components in your estimated model are completely gone just four periods into the future, and the AR(4) effects diminish exponentially. Attempting to forecast these effects 1000 periods into the future is pointless. For AR and MA fluctuations around a mean of 10, the best forecast for anything beyond a relatively short time horizon would be 10. The forecasts you show are perfectly correct. I strongly urge you to check out the book recommended by technocrat. The coverage of the principles of forecasting is the same in both the second (fpp2) and third (fpp3) editions. They differ only in the R packages used in the examples and exercises, with the second edition based on forecast package and the third on the tidyverts set of packages (including fable, feasts and tsibble). 2 Likes Thanks a lot technocrat and EconProf for your answers. basically I now use the ARIMA models for forecasts of size h = 10 for the first 200 hours and they look okay. How can I increase the h such that I can have forecasts for h = 24 (one day)? Bascially when I use h = 24 all models again produce just a more or less constant line after h = 10. Do I have to increase the order of p,d,q in ARIMA (p,d,q)? 1 Like I'm currently working through an analysis of half-hourly electrical metering data. I've been aggregating to monthly and daily and training the prior two years then testing the forecast diagnostics by successively reducing the horizon. As your experience shows, the prediction interval (confidence bands) narrow as h approaches unity. As h increases, the width of prediction intervals exceeds the range of the input data and can even become negative. Day ahead forecasting is possible, certainly, but it should be done with daily data, not hourly. Much of the information in the data at hourly or half-hourly intervals is masked by random variation. Hyndman comments on this, particularly in the examples based on GOOG stock movement. Thanks technocrat for your answer and effort, you wrote that "Day ahead forecasting is possible, certainly, but it should be done with daily data, not hourly. ". What do you exactly mean with daily data? Basically what I need is a daily forecast with hourly values. The time series model should forecast the prices for the next 24 hours. So always at the beginning of the day (0:00) I'd like to have 24 values of prices (one for each hour). Is something like this not possible with ARIMA modells? That's doable. The idea is that you should try to set the forecast horizon h too much greater than the data frequency, f. For h = f, that shouldn't be a problem, per se, provided, of course, that any pre-whitening has been done and other requirements are met. Thanks technocrat for your comment, I really appreciate your effort I have several questions regarding to it: 1. What do you mean by dataf frequency? Basically I would like to have a forecast every 24 hours. So is the frequency then 24 hours? 2. What do you mean by "pre-whitening"? Maybe making the time series stationary? 3. Which other requirements are you referring to? f is how often the data is observed. 48 for half-hourly, 24 hourly, etc. This is set with my_ts <- ts(my_vector, start = (2020,1,1), frequency = 24)  My bad on whitening, I've been reading Shumway and Stoffer who use that terminology for differencing, etc. to make a series stationary. See §8.1 of Hyndman §8.3 of Hyndman gives the requirements for when you can't get to a stationary representation. As he explains in §8.7, when using auto_arima, you still have to • Examine the ACF/PACF: Is an ARIMA(p,d,0) or ARIMA(0,d,q* ) model appropriate? • Try your chosen model(s), and use the AICc to search for a better model. • Check the residuals from your chosen model by plotting the ACF of the residuals, and doing a portmanteau test of the residuals. If they do not look like white noise, try a modified model. • Once the residuals look like white noise, calculate forecasts. 1 Like Thanks technocrat for your answer, so in my example I have hourly data so the frequency would be 24 if I understood your post correctly. In the previous post you wrote " The idea is that you should try to set the forecast horizon h too much greater than the data frequency, f" On another post you wrote that h should not be too high and I have also experienced bad results for h greater than 10. So what can I do if I have a too high frequency for a given h? were you able to make the data stationary? Thanks technocrat for your answer and effort, I really appreciate it Basically I have calculated an auto.arima() model and I have the following output autoArima_model<-auto.arima(ts(energyData$Price[0:200]))
autoArima_model
Series: ts(energyData$Price[0:200]) ARIMA(4,1,2) Coefficients: ar1 ar2 ar3 ar4 ma1 ma2 0.2929 0.9292 -0.1550 -0.2718 -0.0439 -0.8273 s.e. 0.1136 0.1245 0.0793 0.0718 0.1019 0.1033 sigma^2 estimated as 39.85: log likelihood=-646.36 AIC=1306.71 AICc=1307.3 BIC=1329.76 So it suggests an ARIMA(4,1,2) model. As the d part is non-negative, I think the auto.arima() function should have made the time series stationary for its calculations. Regarding the ACF and PACF plots I have to say that the ACF plot shows high-positive values for values until about 30 which would mean that a ARIMA(4,1,2) model is wrong but I always get very high ACF values for my data while auto.arima() derives lower grade models. I have attached the ACF/PACF plots So my basic question sitll persists: How can I increase the h (from 10 to 24) while still having good forecast values. @technocrat: Any comments on my last post? I'd highly appreciate any further comment from you and would be quite thankful if you could share your experience. 1 Like Sorry. auto.arima is not infallible (see Hyndman §8.7) and its model should be modified if Check the residuals from your chosen model by plotting the ACF of the residuals, and doing a portmanteau test of the residuals. If they do not look like white noise, try a modified model. Once the residuals look like white noise, calculate forecasts. With a non-zero \sigma^2 and second-degree differencing, long-term forecasts follow a quadratic trend. Hyndman § 8.6. Hyndman §8.5 matches the plots of your model The data may follow an ARIMA(p,d,0) model if the ACF and PACF plots of the differenced data show the following patterns: • the ACF is exponentially decaying or sinusoidal; • there is a significant spike at lag in the PACF, but none beyond lag p suggesting that the moving average term should be zero. Maybe? Do you have some representative data? Thanks technocrat for your comment and sorry for my late reply (Christmas vacation), bascially I do not really understand your answer and I still do not know how I can make a forecast for the next 24 hours. I just added 1000 datapoints for the forecast as you asked for representative data. The data has a hourly resolution. 10.07 -4.08 -9.91 -7.41 -12.55 -17.25 -15.07 -4.93 -6.33 -4.93 0.45 0.12 -0.02 0 -0.03 1.97 9.06 0.07 -4.97 -6.98 -24.93 -4.87 -28.93 -33.57 -45.92 -48.29 -44.99 -48.93 -29.91 -0.01 37.43 48.06 50.74 47.57 43.94 40.97 44.95 49.64 53.67 56.01 56.95 62.08 62.11 57.99 55.64 55.13 50.76 42.91 45.22 45.63 44 43.88 45.92 51.07 52.77 62.89 60.03 58.19 62.99 63.52 64.67 65.24 67.76 68.41 69.55 67.28 69.46 68.38 61.72 53.72 49.98 50.73 47.11 47.07 46.94 47 46.91 49.59 55.32 55.78 55.52 55.23 53.58 51.74 51.6 51.41 51.69 52.59 54.66 54.1 51.89 46.58 45.43 43.96 31.41 26.9 25.12 24.12 22.04 18.37 22.09 23.35 28.76 36.63 40.46 45.85 49.8 51.36 51.74 51.92 53.22 56.62 56.92 61.64 59.44 52.75 51.9 51.38 49.96 50.29 47.72 48.38 48.02 44.23 47.17 48.19 49.11 50.44 53.4 56.55 60.02 60.22 55 52.39 51.57 54.71 63.43 67.37 67.2 66.03 55.36 58.59 53.7 46.03 47.98 47.84 46.11 46.08 47.62 55.77 68.61 74.15 74.93 73.59 71.23 68.79 66.75 62.47 53.25 53.26 53.42 47.91 42.05 40.96 32.04 20.82 1.84 17.94 20.91 7.78 14.33 18.56 18.57 35.81 43.87 46.93 43.88 43.85 46.74 43.94 43.21 43.81 45.6 35.21 45.64 45.63 37.94 39.53 35.97 29.72 22.55 20.04 7.24 3.43 10.04 14.2 25.41 36.98 43.89 50.98 50.3 50.21 45.8 43.64 43.49 45.05 49.95 53.82 55.94 54.19 54.19 52.98 51.68 48.95 47.63 46.48 49.08 47.11 48.8 48.5 49.8 56.97 72.6 81.17 81.79 81.15 80 76.39 75 76.06 75.4 76.78 83.45 85.15 78.16 71.92 67.43 60.49 54.45 47.84 46.58 45.74 43.81 46.24 46.46 51.29 60.53 60.02 62.92 61.58 60.6 51.89 50.8 45.46 42.2 45 51.51 48.59 49.2 44.99 44.93 46.4 43.46 47.24 45.88 44.61 42.9 42.03 39.33 42.98 41.23 42.12 45.1 44.27 44.18 42.05 38.61 37.76 33.03 35.84 44.24 46.29 40.05 38.15 30.26 40.2 30.89 23.35 12.4 19.01 23.51 23.25 20.01 20.8 22.14 23.68 25.05 26.3 30.81 27.06 8.5 -0.08 -0.01 -0.27 21.55 24.04 15.91 -0.02 -0.33 3.04 -7.28 -3.14 -12 -15.04 -11.09 -4.94 1.59 35.9 51.22 51.66 46.09 50.26 44.49 41.24 38.91 41.79 49.63 50.93 51.25 50.93 50.52 49.61 44.3 41.29 37.26 35.18 35.82 34.99 34.56 30.8 33.41 47.4 53.73 57.02 58.38 56.54 55 53.92 53.31 52.63 52.12 51 53.91 46.35 42.73 40.32 39.93 39.66 32.04 29.23 29.26 32.64 32.41 33.06 33.22 46.63 53.8 55.73 53.83 54.22 54.92 53.65 52.69 53.55 53.72 54.83 54.01 52.67 50.92 39.81 39.29 39.91 30.77 26.35 25.23 22.77 24.02 25.63 28.06 35.38 46.04 49.96 50.56 46.1 46.02 43.94 46.64 55 55.9 57.43 58.72 55.06 53.85 49.52 42.35 42.44 41.11 42.26 40.91 43.77 45.05 47.89 49.79 54.77 73.08 76.86 74.32 70.51 69.02 68.94 65.27 67.44 68.14 69 75.04 76.13 72.46 61.83 59.24 56.7 53.16 52.47 53.18 54.73 51.15 52.25 50.93 49.04 50.56 55.71 56.62 63.13 57.33 55.26 54.38 52.55 55.41 62.93 67.15 68.91 57.37 54.61 52.32 51.53 49.8 47.74 47.01 48.8 48.9 49.63 49.53 46.69 46.76 50.88 52.38 50.07 49.33 48.98 47.4 53.2 55.39 58.02 66.68 69.43 69.75 67.83 57.37 59.56 52.07 55.54 53.02 52.1 51.31 51.14 53.23 67.16 79.62 88.06 86.01 80.04 75 70.65 71.56 72.03 76.49 81.08 88.35 88.5 79.93 73.69 68.34 59.83 54.93 54.19 54.05 53.52 51.97 51.96 52.03 62.33 73.91 80.23 76.6 74.19 70.06 62.07 58.45 59.69 65.09 70.21 78.8 74.13 64.46 58.34 55.38 56.59 53.72 53.2 53.92 53.11 51.19 51.02 55.05 63.96 82.55 92.15 93.65 91.93 88.07 84.96 83.99 85.59 86.87 84.96 90.01 93.76 90.54 78.49 68.93 65.1 59 61.88 59.08 57.9 56.37 56.95 61.53 75.98 99.07 113.07 111.23 108.25 103.69 98.33 92.63 89.86 90.45 95.01 106.82 121.46 102.06 89.34 74.43 69.92 63.79 64.63 60.64 57.8 55.44 55.28 58.98 71.1 84.48 97 93.64 85 78 71.81 69.87 68.64 65.58 65.08 70.87 71.74 58.51 50.78 51.12 47.16 42.9 44.29 42.61 42.51 41.96 41.65 40.72 41.53 42.36 51.37 56.28 58.62 59.18 56.72 57.44 53.32 52.8 52.44 55.95 57.92 53.78 47.98 44.99 43.9 37.75 28.42 24.48 19.98 18.13 13.61 15.34 3.65 24.47 27.04 32.97 39.06 44.05 46.86 42.97 41.18 40.5 41.18 47.25 52.64 52.96 50.44 47.74 50.82 44.81 42.4 40.97 40.42 37.8 38.09 41.83 53.09 64.04 64.88 62.2 58.38 55.92 55.12 51.36 48.63 47.34 48.83 60.83 59.64 54.37 47.54 45.69 46.36 43.91 44.87 48.07 47.49 44.59 45.97 46.46 55.82 67.67 72.8 70.63 70.38 67.51 64.03 63.84 66.56 66 69.33 71.45 72.82 69.81 59.51 55.74 53.83 49.34 46.52 46.36 45.1 42.85 42.74 46.34 52.09 64.99 68.17 66.76 59.06 53.03 54.19 49.7 53.56 49.37 52.65 60.49 62.53 59.28 50.02 51.01 50.08 46.86 47.17 45.47 45.43 46.82 45.08 48.7 62.41 71.7 78.49 69.08 67.35 67.94 66.81 66.22 66.16 66.16 65.35 66.68 66.98 65.4 59.9 52.84 51.36 46.72 43.55 45.47 43.1 41.1 41.51 45.76 47.72 54.04 56.66 55.93 55.51 56.9 58.51 64 64.93 64.55 63.91 67.71 69.98 67.34 62.99 55.32 54.01 50.92 50.04 47.07 45.7 43.14 42.71 42.6 43.47 47.68 51.47 55.79 59.5 59.15 56.08 50.33 49.09 48.6 49.39 56.86 57.91 54.59 50.43 48.48 48.49 46.06 44.8 41.29 40.48 39 38.9 38.47 39.88 40.97 43.8 47.05 49.33 51.11 50.35 47.09 45.14 44.4 45.79 51.94 59.54 57.94 54.69 51.94 51.04 46.42 43.7 41.37 40.48 39.21 38.55 40.71 49.05 60.67 58.93 55.47 48.15 43.46 41.69 40.47 41.01 43.37 43.82 49.06 46.78 42.08 39.42 38.75 38.52 32.51 34.49 36.71 38.42 39.39 39.82 41.72 50.31 61.52 62.31 62.31 64.56 62.85 58.63 57.81 57.96 58.04 60.35 63.06 65.79 62.63 61.77 53.38 50.12 46.98 42.8 41.63 40.98 40.4 41.38 42.31 51.1 57.59 59.01 53.34 53.54 53.32 53.19 52.81 52.93 54.3 56.73 60.27 62.4 59.62 55.8 50.49 49.5 46.18 46.25 45.21 41.24 38.05 37 39.04 48.06 53.29 53.04 51.98 51.47 46.38 43.24 42.32 40.75 41.63 41.77 51.37 51.4 45.75 46.07 41.47 38.71 33.68 32.73 33.24 32.57 31.16 35.71 38.99 45.85 56.33 57.94 54.91 51.35 50.66 44.59 39.51 38.77 42.31 43.98 49.61 50.66 46.04 35.88 33.96 27.56 24.47 0.02 0.08 -4.09 -4 0.06 0.09 0.05 6.37 12.08 11.18 10.64 4.12 0.05 -0.1 -2.05 0.09 9.64 26.97 36.17 25.97 16.96 0.09 12.91 0.09 22.88 16.16 16.02 12.45 10.19 14.51 7.01 10.98 20.62 32.95 36.75 38.68 32.5 34 31.2 27.99 32.61 39.41 49.19 49.48 43.1 28.99 34.37 30.58 -0.01 -1.22 -0.04 -3.54 -3.95 5.65 39.91 49.04 49.41 44.99 39.94 38.73 36.59 38.54 38.66 38.86 38.61 Follow the fpp3 text with these data. Notice some things: 1. Better performance when limiting the training data to 200 points 2. Consistent weekly seasonality, but shifting daily 3. Data require differencing 4. Among the baseline model SNAIVE works best (tomorrow will be like yesterday) 5. An ARIMA model beats it slightly. suppressPackageStartupMessages({ library(fpp3) }) Time = seq( from=as.POSIXct("2021-1-1 0:00", tz="UTC"), to=as.POSIXct("2021-2-11 15:00", tz="UTC"), by="hour") DAT <- data.frame(Time, observed = c(10.07, -4.08, -9.91, -7.41, -12.55, -17.25, -15.07, -4.93, -6.33, -4.93, 0.45, 0.12, -0.02, 0, -0.03, 1.97, 9.06, 0.07, -4.97, -6.98, -24.93, -4.87, -28.93, -33.57, -45.92, -48.29, -44.99, -48.93, -29.91, -0.01, 37.43, 48.06, 50.74, 47.57, 43.94, 40.97, 44.95, 49.64, 53.67, 56.01, 56.95, 62.08, 62.11, 57.99, 55.64, 55.13, 50.76, 42.91, 45.22, 45.63, 44, 43.88, 45.92, 51.07, 52.77, 62.89, 60.03, 58.19, 62.99, 63.52, 64.67, 65.24, 67.76, 68.41, 69.55, 67.28, 69.46, 68.38, 61.72, 53.72, 49.98, 50.73, 47.11, 47.07, 46.94, 47, 46.91, 49.59, 55.32, 55.78, 55.52, 55.23, 53.58, 51.74, 51.6, 51.41, 51.69, 52.59, 54.66, 54.1, 51.89, 46.58, 45.43, 43.96, 31.41, 26.9, 25.12, 24.12, 22.04, 18.37, 22.09, 23.35, 28.76, 36.63, 40.46, 45.85, 49.8, 51.36, 51.74, 51.92, 53.22, 56.62, 56.92, 61.64, 59.44, 52.75, 51.9, 51.38, 49.96, 50.29, 47.72, 48.38, 48.02, 44.23, 47.17, 48.19, 49.11, 50.44, 53.4, 56.55, 60.02, 60.22, 55, 52.39, 51.57, 54.71, 63.43, 67.37, 67.2, 66.03, 55.36, 58.59, 53.7, 46.03, 47.98, 47.84, 46.11, 46.08, 47.62, 55.77, 68.61, 74.15, 74.93, 73.59, 71.23, 68.79, 66.75, 62.47, 53.25, 53.26, 53.42, 47.91, 42.05, 40.96, 32.04, 20.82, 1.84, 17.94, 20.91, 7.78, 14.33, 18.56, 18.57, 35.81, 43.87, 46.93, 43.88, 43.85, 46.74, 43.94, 43.21, 43.81, 45.6, 35.21, 45.64, 45.63, 37.94, 39.53, 35.97, 29.72, 22.55, 20.04, 7.24, 3.43, 10.04, 14.2, 25.41, 36.98, 43.89, 50.98, 50.3, 50.21, 45.8, 43.64, 43.49, 45.05, 49.95, 53.82, 55.94, 54.19, 54.19, 52.98, 51.68, 48.95, 47.63, 46.48, 49.08, 47.11, 48.8, 48.5, 49.8, 56.97, 72.6, 81.17, 81.79, 81.15, 80, 76.39, 75, 76.06, 75.4, 76.78, 83.45, 85.15, 78.16, 71.92, 67.43, 60.49, 54.45, 47.84, 46.58, 45.74, 43.81, 46.24, 46.46, 51.29, 60.53, 60.02, 62.92, 61.58, 60.6, 51.89, 50.8, 45.46, 42.2, 45, 51.51, 48.59, 49.2, 44.99, 44.93, 46.4, 43.46, 47.24, 45.88, 44.61, 42.9, 42.03, 39.33, 42.98, 41.23, 42.12, 45.1, 44.27, 44.18, 42.05, 38.61, 37.76, 33.03, 35.84, 44.24, 46.29, 40.05, 38.15, 30.26, 40.2, 30.89, 23.35, 12.4, 19.01, 23.51, 23.25, 20.01, 20.8, 22.14, 23.68, 25.05, 26.3, 30.81, 27.06, 8.5, -0.08, -0.01, -0.27, 21.55, 24.04, 15.91, -0.02, -0.33, 3.04, -7.28, -3.14, -12, -15.04, -11.09, -4.94, 1.59, 35.9, 51.22, 51.66, 46.09, 50.26, 44.49, 41.24, 38.91, 41.79, 49.63, 50.93, 51.25, 50.93, 50.52, 49.61, 44.3, 41.29, 37.26, 35.18, 35.82, 34.99, 34.56, 30.8, 33.41, 47.4, 53.73, 57.02, 58.38, 56.54, 55, 53.92, 53.31, 52.63, 52.12, 51, 53.91, 46.35, 42.73, 40.32, 39.93, 39.66, 32.04, 29.23, 29.26, 32.64, 32.41, 33.06, 33.22, 46.63, 53.8, 55.73, 53.83, 54.22, 54.92, 53.65, 52.69, 53.55, 53.72, 54.83, 54.01, 52.67, 50.92, 39.81, 39.29, 39.91, 30.77, 26.35, 25.23, 22.77, 24.02, 25.63, 28.06, 35.38, 46.04, 49.96, 50.56, 46.1, 46.02, 43.94, 46.64, 55, 55.9, 57.43, 58.72, 55.06, 53.85, 49.52, 42.35, 42.44, 41.11, 42.26, 40.91, 43.77, 45.05, 47.89, 49.79, 54.77, 73.08, 76.86, 74.32, 70.51, 69.02, 68.94, 65.27, 67.44, 68.14, 69, 75.04, 76.13, 72.46, 61.83, 59.24, 56.7, 53.16, 52.47, 53.18, 54.73, 51.15, 52.25, 50.93, 49.04, 50.56, 55.71, 56.62, 63.13, 57.33, 55.26, 54.38, 52.55, 55.41, 62.93, 67.15, 68.91, 57.37, 54.61, 52.32, 51.53, 49.8, 47.74, 47.01, 48.8, 48.9, 49.63, 49.53, 46.69, 46.76, 50.88, 52.38, 50.07, 49.33, 48.98, 47.4, 53.2, 55.39, 58.02, 66.68, 69.43, 69.75, 67.83, 57.37, 59.56, 52.07, 55.54, 53.02, 52.1, 51.31, 51.14, 53.23, 67.16, 79.62, 88.06, 86.01, 80.04, 75, 70.65, 71.56, 72.03, 76.49, 81.08, 88.35, 88.5, 79.93, 73.69, 68.34, 59.83, 54.93, 54.19, 54.05, 53.52, 51.97, 51.96, 52.03, 62.33, 73.91, 80.23, 76.6, 74.19, 70.06, 62.07, 58.45, 59.69, 65.09, 70.21, 78.8, 74.13, 64.46, 58.34, 55.38, 56.59, 53.72, 53.2, 53.92, 53.11, 51.19, 51.02, 55.05, 63.96, 82.55, 92.15, 93.65, 91.93, 88.07, 84.96, 83.99, 85.59, 86.87, 84.96, 90.01, 93.76, 90.54, 78.49, 68.93, 65.1, 59, 61.88, 59.08, 57.9, 56.37, 56.95, 61.53, 75.98, 99.07, 113.07, 111.23, 108.25, 103.69, 98.33, 92.63, 89.86, 90.45, 95.01, 106.82, 121.46, 102.06, 89.34, 74.43, 69.92, 63.79, 64.63, 60.64, 57.8, 55.44, 55.28, 58.98, 71.1, 84.48, 97, 93.64, 85, 78, 71.81, 69.87, 68.64, 65.58, 65.08, 70.87, 71.74, 58.51, 50.78, 51.12, 47.16, 42.9, 44.29, 42.61, 42.51, 41.96, 41.65, 40.72, 41.53, 42.36, 51.37, 56.28, 58.62, 59.18, 56.72, 57.44, 53.32, 52.8, 52.44, 55.95, 57.92, 53.78, 47.98, 44.99, 43.9, 37.75, 28.42, 24.48, 19.98, 18.13, 13.61, 15.34, 3.65, 24.47, 27.04, 32.97, 39.06, 44.05, 46.86, 42.97, 41.18, 40.5, 41.18, 47.25, 52.64, 52.96, 50.44, 47.74, 50.82, 44.81, 42.4, 40.97, 40.42, 37.8, 38.09, 41.83, 53.09, 64.04, 64.88, 62.2, 58.38, 55.92, 55.12, 51.36, 48.63, 47.34, 48.83, 60.83, 59.64, 54.37, 47.54, 45.69, 46.36, 43.91, 44.87, 48.07, 47.49, 44.59, 45.97, 46.46, 55.82, 67.67, 72.8, 70.63, 70.38, 67.51, 64.03, 63.84, 66.56, 66, 69.33, 71.45, 72.82, 69.81, 59.51, 55.74, 53.83, 49.34, 46.52, 46.36, 45.1, 42.85, 42.74, 46.34, 52.09, 64.99, 68.17, 66.76, 59.06, 53.03, 54.19, 49.7, 53.56, 49.37, 52.65, 60.49, 62.53, 59.28, 50.02, 51.01, 50.08, 46.86, 47.17, 45.47, 45.43, 46.82, 45.08, 48.7, 62.41, 71.7, 78.49, 69.08, 67.35, 67.94, 66.81, 66.22, 66.16, 66.16, 65.35, 66.68, 66.98, 65.4, 59.9, 52.84, 51.36, 46.72, 43.55, 45.47, 43.1, 41.1, 41.51, 45.76, 47.72, 54.04, 56.66, 55.93, 55.51, 56.9, 58.51, 64, 64.93, 64.55, 63.91, 67.71, 69.98, 67.34, 62.99, 55.32, 54.01, 50.92, 50.04, 47.07, 45.7, 43.14, 42.71, 42.6, 43.47, 47.68, 51.47, 55.79, 59.5, 59.15, 56.08, 50.33, 49.09, 48.6, 49.39, 56.86, 57.91, 54.59, 50.43, 48.48, 48.49, 46.06, 44.8, 41.29, 40.48, 39, 38.9, 38.47, 39.88, 40.97, 43.8, 47.05, 49.33, 51.11, 50.35, 47.09, 45.14, 44.4, 45.79, 51.94, 59.54, 57.94, 54.69, 51.94, 51.04, 46.42, 43.7, 41.37, 40.48, 39.21, 38.55, 40.71, 49.05, 60.67, 58.93, 55.47, 48.15, 43.46, 41.69, 40.47, 41.01, 43.37, 43.82, 49.06, 46.78, 42.08, 39.42, 38.75, 38.52, 32.51, 34.49, 36.71, 38.42, 39.39, 39.82, 41.72, 50.31, 61.52, 62.31, 62.31, 64.56, 62.85, 58.63, 57.81, 57.96, 58.04, 60.35, 63.06, 65.79, 62.63, 61.77, 53.38, 50.12, 46.98, 42.8, 41.63, 40.98, 40.4, 41.38, 42.31, 51.1, 57.59, 59.01, 53.34, 53.54, 53.32, 53.19, 52.81, 52.93, 54.3, 56.73, 60.27, 62.4, 59.62, 55.8, 50.49, 49.5, 46.18, 46.25, 45.21, 41.24, 38.05, 37, 39.04, 48.06, 53.29, 53.04, 51.98, 51.47, 46.38, 43.24, 42.32, 40.75, 41.63, 41.77, 51.37, 51.4, 45.75, 46.07, 41.47, 38.71, 33.68, 32.73, 33.24, 32.57, 31.16, 35.71, 38.99, 45.85, 56.33, 57.94, 54.91, 51.35, 50.66, 44.59, 39.51, 38.77, 42.31, 43.98, 49.61, 50.66, 46.04, 35.88, 33.96, 27.56, 24.47, 0.02, 0.08, -4.09, -4, 0.06, 0.09, 0.05, 6.37, 12.08, 11.18, 10.64, 4.12, 0.05, -0.1, -2.05, 0.09, 9.64, 26.97, 36.17, 25.97, 16.96, 0.09, 12.91, 0.09, 22.88, 16.16, 16.02, 12.45, 10.19, 14.51, 7.01, 10.98, 20.62, 32.95, 36.75, 38.68, 32.5, 34, 31.2, 27.99, 32.61, 39.41, 49.19, 49.48, 43.1, 28.99, 34.37, 30.58, -0.01, -1.22, -0.04, -3.54, -3.95, 5.65, 39.91, 49.04, 49.41, 44.99, 39.94, 38.73, 36.59, 38.54, 38.66, 38.86, 38.61) ) dat <- tsibble(DAT,index=Time) autoplot(dat) + theme_minimal() #> Plot variable not specified, automatically selected .vars = observed  dat %>% features(observed, feature_set(pkgs="feasts")) %>% t() #> Warning: n_flat_spots() is deprecated as of feasts 0.1.5. #> Please use longest_flat_spot() instead. #> This warning is displayed once every 8 hours. #> Call lifecycle::last_warnings() to see where this warning was generated. #> [,1] #> trend_strength 0.88495741 #> seasonal_strength_day 0.66506048 #> seasonal_peak_day 18.00000000 #> seasonal_trough_day 4.00000000 #> spikiness 0.00993752 #> linearity 40.49057987 #> curvature -302.36974156 #> stl_e_acf1 0.79002898 #> stl_e_acf10 1.10419120 #> acf1 0.96037775 #> acf10 4.92120104 #> diff1_acf1 0.34763257 #> diff1_acf10 0.22523960 #> diff2_acf1 -0.34315344 #> diff2_acf10 0.14589570 #> season_acf1 0.47364536 #> pacf5 1.07745209 #> diff1_pacf5 0.29758087 #> diff2_pacf5 0.48252887 #> season_pacf -0.21001083 #> zero_run_mean 1.00000000 #> nonzero_squared_cv 0.20238202 #> zero_start_prop 0.00000000 #> zero_end_prop 0.00000000 #> lambda_guerrero 1.37960539 #> kpss_stat 0.86876471 #> kpss_pvalue 0.01000000 #> pp_stat -5.49506838 #> pp_pvalue 0.01000000 #> ndiffs 1.00000000 #> nsdiffs 1.00000000 #> bp_stat 922.32541540 #> bp_pvalue 0.00000000 #> lb_stat 925.09516140 #> lb_pvalue 0.00000000 #> var_tiled_var 0.31164518 #> var_tiled_mean 0.65299865 #> shift_level_max 63.92541667 #> shift_level_index 31.00000000 #> shift_var_max 1803.47726444 #> shift_var_index 21.00000000 #> shift_kl_max 140.50454837 #> shift_kl_index 30.00000000 #> spectral_entropy 0.65228686 #> n_crossing_points 90.00000000 #> longest_flat_spot 27.00000000 #> coef_hurst 0.99958586 #> stat_arch_lm 0.86069565 dat %>% ACF(observed) %>% autoplot() + theme_minimal()  lambda <- dat %>% features(observed, features = guerrero) %>% pull(lambda_guerrero) dat %>% autoplot(box_cox(observed, lambda)) + theme_minimal()  dcmp <- dat %>% model(STL(observed)) components(dcmp) #> # A dable: 1,000 x 8 [1h] <UTC> #> # Key: .model [1] #> # STL Decomposition: observed = trend + season_week + season_day + remainder #> .model Time observed trend season_week season_day remainder #> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 STL(o… 2021-01-01 00:00:00 10.1 5.05 -12.4 -0.228 17.6 #> 2 STL(o… 2021-01-01 01:00:00 -4.08 4.59 -18.0 -2.16 11.5 #> 3 STL(o… 2021-01-01 02:00:00 -9.91 4.13 -18.9 -1.97 6.84 #> 4 STL(o… 2021-01-01 03:00:00 -7.41 3.68 -18.0 -2.98 9.89 #> 5 STL(o… 2021-01-01 04:00:00 -12.6 3.22 -18.3 -2.01 4.54 #> 6 STL(o… 2021-01-01 05:00:00 -17.2 2.77 -9.21 -2.85 -7.95 #> 7 STL(o… 2021-01-01 06:00:00 -15.1 2.41 -1.19 -3.29 -13.0 #> 8 STL(o… 2021-01-01 07:00:00 -4.93 2.05 3.90 -2.09 -8.80 #> 9 STL(o… 2021-01-01 08:00:00 -6.33 1.70 2.21 -1.50 -8.73 #> 10 STL(o… 2021-01-01 09:00:00 -4.93 1.34 1.60 -0.464 -7.40 #> # … with 990 more rows, and 1 more variable: season_adjust <dbl> autoplot(dat, observed, color = "gray") + autolayer(components(dcmp), trend, color = "red") + theme_minimal()  components(dcmp) %>% autoplot() + theme_minimal()  # Break off held-out past <- head(dat,832) future <- tail(dat,24*7) bench_fit <- past %>% model( Mean = MEAN(observed), Naïve = NAIVE(observed), Drift = NAIVE(observed ~ drift()), Seasonal Naïve = SNAIVE(observed)) # Produce forecasts mid-February 2021 fc <- bench_fit %>% forecast(new_data = future) fc %>% bench_fit <- past %>% model( Mean = MEAN(observed), Naïve = NAIVE(observed), Drift = NAIVE(observed ~ drift()), Seasonal Naïve = SNAIVE(observed)) #> Error in fc %>% bench_fit <- past %>% model(Mean = MEAN(observed), Naïve = NAIVE(observed), : could not find function "%>%<-" autoplot(past, level = NULL) + autolayer(future, observed, color = "black") + guides(colour = guide_legend(title = "Forecasts")) + theme_minimal() #> Plot variable not specified, automatically selected .vars = observed #> Warning: Ignoring unknown parameters: level  accuracy(fc,future) #> # A tibble: 4 x 9 #> .model .type ME RMSE MAE MPE MAPE MASE ACF1 #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Drift Test -10.6 22.5 16.5 -666. 8844. NaN 0.942 #> 2 Mean Test -12.8 22.7 16.4 -816. 8919. NaN 0.936 #> 3 Naïve Test -7.13 20.0 14.6 -712. 7900. NaN 0.936 #> 4 Seasonal Naïve Test -11.0 20.7 14.4 -815. 7833. NaN 0.942 # Plot the diagnostics past %>% model( Mean = MEAN(observed)) %>% gg_tsresiduals()  past %>% model(Naïve = NAIVE(observed)) %>% gg_tsresiduals() #> Warning: Removed 1 row(s) containing missing values (geom_path). #> Warning: Removed 1 rows containing missing values (geom_point). #> Warning: Removed 1 rows containing non-finite values (stat_bin).  past %>% model(Drift = NAIVE(observed ~ drift())) %>% gg_tsresiduals() #> Warning: Removed 1 row(s) containing missing values (geom_path). #> Warning: Removed 1 rows containing missing values (geom_point). #> Warning: Removed 1 rows containing non-finite values (stat_bin).  past %>% model(Seasonal Naïve = SNAIVE(observed)) %>% gg_tsresiduals() #> Warning: Removed 24 row(s) containing missing values (geom_path). #> Warning: Removed 24 rows containing missing values (geom_point). #> Warning: Removed 24 rows containing non-finite values (stat_bin).  past %>% model(MEAN(observed)) %>% forecast(h = 48) %>% autoplot(past)  past %>% model(NAIVE(observed)) %>% forecast(h = 48) %>% autoplot(past)  past %>% model(Drift = NAIVE(observed ~ drift())) %>% forecast(h = 48) %>% autoplot(past)  past %>% model(SNAIVE(observed)) %>% forecast(h = 48) %>% autoplot(past)  augment(bench_fit) %>% features(.innov, box_pierce, lag = 24, dof = 0) #> # A tibble: 4 x 3 #> .model bp_stat bp_pvalue #> <chr> <dbl> <dbl> #> 1 Drift 501. 0 #> 2 Mean 5813. 0 #> 3 Naïve 501. 0 #> 4 Seasonal Naïve 4708. 0 short_past <- tail(past,200) bench_fit2 <- short_past %>% model( Mean = MEAN(observed), Naïve = NAIVE(observed), Drift = NAIVE(observed ~ drift()), Seasonal Naïve = SNAIVE(observed)) fc2 <- bench_fit2 %>% forecast(new_data = future) fc2 %>% autoplot(past, level = NULL) + autolayer(future, observed, color = "black") + guides(colour = guide_legend(title = "Forecasts")) + theme_minimal()  accuracy(fc2,future) #> # A tibble: 4 x 9 #> .model .type ME RMSE MAE MPE MAPE MASE ACF1 #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Drift Test -11.7 23.5 17.3 -650. 9168. NaN 0.945 #> 2 Mean Test -15.6 24.4 18.0 -870. 9441. NaN 0.936 #> 3 Naïve Test -7.13 20.0 14.6 -712. 7900. NaN 0.936 #> 4 Seasonal Naïve Test -11.0 20.7 14.4 -815. 7833. NaN 0.942 short_past %>% model( Mean = MEAN(observed)) %>% gg_tsresiduals()  short_past %>% model(Naïve = NAIVE(observed)) %>% gg_tsresiduals() #> Warning: Removed 1 row(s) containing missing values (geom_path). #> Warning: Removed 1 rows containing missing values (geom_point). #> Warning: Removed 1 rows containing non-finite values (stat_bin).  short_past %>% model(Drift = NAIVE(observed ~ drift())) %>% gg_tsresiduals() #> Warning: Removed 1 row(s) containing missing values (geom_path). #> Warning: Removed 1 rows containing missing values (geom_point). #> Warning: Removed 1 rows containing non-finite values (stat_bin).  short_past %>% model(Seasonal Naïve = SNAIVE(observed)) %>% gg_tsresiduals() #> Warning: Removed 24 row(s) containing missing values (geom_path). #> Warning: Removed 24 rows containing missing values (geom_point). #> Warning: Removed 24 rows containing non-finite values (stat_bin).  short_past %>% model(MEAN(observed)) %>% forecast(h = 48) %>% autoplot(short_past)  short_past %>% model(NAIVE(observed)) %>% forecast(h = 48) %>% autoplot(short_past)  short_past %>% model(Drift = NAIVE(observed ~ drift())) %>% forecast(h = 48) %>% autoplot(short_past)  short_past %>% model(SNAIVE(observed)) %>% forecast(h = 48) %>% autoplot(short_past)  short_past %>% features(observed, unitroot_kpss) #> # A tibble: 1 x 2 #> kpss_stat kpss_pvalue #> <dbl> <dbl> #> 1 0.381 0.0856 short_past %>% mutate(diff_observed = difference(observed)) %>% features(diff_observed, unitroot_kpss) #> # A tibble: 1 x 2 #> kpss_stat kpss_pvalue #> <dbl> <dbl> #> 1 0.0564 0.1 short_past %>% mutate(diff_observed = difference(observed)) -> d bench_fitd <- past %>% model( Mean = MEAN(observed), Naïve = NAIVE(observed), Drift = NAIVE(observed ~ drift()), Seasonal Naïve = SNAIVE(observed)) fcd <- bench_fitd %>% forecast(new_data = future) fcd %>% autoplot(d, level = NULL) + autolayer(future, observed, color = "black") + guides(colour = guide_legend(title = "Forecasts")) + theme_minimal()  accuracy(fcd,future) #> # A tibble: 4 x 9 #> .model .type ME RMSE MAE MPE MAPE MASE ACF1 #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Drift Test -10.6 22.5 16.5 -666. 8844. NaN 0.942 #> 2 Mean Test -12.8 22.7 16.4 -816. 8919. NaN 0.936 #> 3 Naïve Test -7.13 20.0 14.6 -712. 7900. NaN 0.936 #> 4 Seasonal Naïve Test -11.0 20.7 14.4 -815. 7833. NaN 0.942 # Plot the diagnostics d %>% model(Mean = MEAN(observed)) %>% gg_tsresiduals()  d %>% model(Naïve = NAIVE(observed)) %>% gg_tsresiduals() #> Warning: Removed 1 row(s) containing missing values (geom_path). #> Warning: Removed 1 rows containing missing values (geom_point). #> Warning: Removed 1 rows containing non-finite values (stat_bin).  d %>% model(Drift = NAIVE(observed ~ drift())) %>% gg_tsresiduals() #> Warning: Removed 1 row(s) containing missing values (geom_path). #> Warning: Removed 1 rows containing missing values (geom_point). #> Warning: Removed 1 rows containing non-finite values (stat_bin).  d %>% model(Seasonal Naïve = SNAIVE(observed)) %>% gg_tsresiduals() #> Warning: Removed 24 row(s) containing missing values (geom_path). #> Warning: Removed 24 rows containing missing values (geom_point). #> Warning: Removed 24 rows containing non-finite values (stat_bin).  d %>% model(MEAN(observed)) %>% forecast(h = 48) %>% autoplot(d)  d %>% model(NAIVE(observed)) %>% forecast(h = 48) %>% autoplot(d)  d %>% model(Drift = NAIVE(observed ~ drift())) %>% forecast(h = 48) %>% autoplot(d)  d %>% model(SNAIVE(observed)) %>% forecast(h = 48) %>% autoplot(d)  augment(bench_fit) %>% features(.innov, box_pierce, lag = 24, dof = 0) #> # A tibble: 4 x 3 #> .model bp_stat bp_pvalue #> <chr> <dbl> <dbl> #> 1 Drift 501. 0 #> 2 Mean 5813. 0 #> 3 Naïve 501. 0 #> 4 Seasonal Naïve 4708. 0 d %>% model(ARIMA(observed)) -> afit d %>% model(ARIMA(observed)) %>% forecast(h = 48) %>% autoplot(d) + theme_minimal()  d %>% model(ARIMA(observed)) %>% gg_tsresiduals()  augment(afit) %>% features(.innov, ljung_box, lag = 24, dof = 1) #> # A tibble: 1 x 3 #> .model lb_stat lb_pvalue #> <chr> <dbl> <dbl> #> 1 ARIMA(observed) 26.9 0.262 fca <- afit %>% forecast(new_data = future) accuracy(fca,future) #> # A tibble: 1 x 9 #> .model .type ME RMSE MAE MPE MAPE MASE ACF1 #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 ARIMA(observed) Test -0.512 14.9 12.0 -866. 5291. NaN 0.926  Created on 2021-01-08 by the reprex package (v0.3.0.9001) 2 Likes Thanks technocrat for your really detailled answer and your tremendous effort, I have to admit, as I am a beginner, that I do not understand more than 90% of your code. Bascially I just want to use an ARIMA model and I think you can do this easily with R by using for example the code: arima312_model<-Arima(ts(data$Price[0:200]), order=c(3,1,2))
autoArima_model<-auto.arima(ts(data\$Price[0:200]))
forecastedVal_arima312 <- forecast(arima312_model,h=24, level=c(99.5))
forecastedVal_autoArima_model <- forecast(autoArima_model,h=24, level=c(99.5))
plot(forecastedVal_arima312)
plot(forecastedVal_autoArima_model)

While the results are okay for a prediction horizon of about 10 (h=10) the results get bad for h=24. Now I just wanted to know, how I can improve the results by using the Arima models.

You said that the ARIMA model yields better results than the other models that you used. What is the difference between your ARIMA model and my ARIMA model?

model(ARIMA(observed)) %>%
forecast(h = 48) %>%
autoplot(d) + theme_minimal()


Is there a way how I can improve my ARIMA models?

It is to be expected that the accuracy of forecasts degrades with the increase in the forecast horizon. A model that is better at short horizons is not always better at long horizons.

Arima models can be tuned with seven different parameters, a constant c, the order of the autoregressive part, degree of differencing involved and the order of the moving average part p, d, q, respectively, for the values and p, d, q for the residuals. ARIMA does that automatically, but not necessarily optimally. To manually select the best combination of parameters, see §9.7— Hyndman-Khandakar algorithm for automatic ARIMA modelling.

Don't neglect to benchmark against the baseline models, particularly SNAIVE.

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